Security newsletter N°21

#21
WINTER 2012
THE SECURITY NEWSLETTER
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THE SECURITY
NEWSLETTER
#21
In this Issue
Editorial . . . . . . . . . . . . . . . . . . . . . . . . 2
Be Our Guest . . . . . . . . . . . . . . . . . . . 3
The News. . . . . . . . . . . . . . . . . . . . . . . 4
Patriot Act vs. Cloud?. . . . . . . . . . . . . . . . . . . . . . . . . 4
Ghost Click. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
TOR and the Pedophiles. . . . . . . . . . . . . . . . . . . . . . 5
XML Encryption is eXtreMeLy Weak. . . . . . . . . . 5
Duqu: yet another Stuxnet? . . . . . . . . . . . . . . . . . . . 6
WPS: the new WEP?. . . . . . . . . . . . . 6
Carrier IQ . . . . . . . . . . . . . . . . . . . . . . 7
Protecting Computer Generated 3D
Graphics. . . . . . . . . . . . . . . . . . . . . . . . 8
Scrambling 3D Objects. . . . . . . . . . . . . . . . . . . . . . . 8
Watermarking 3D Objects. . . . . . . . . . . . . . . . . . . . 11
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 13
Where Will We Be? . . . . . . . . . . . . . . 14
Technicolor Sponsored Conferences. . 14
Published Quarterly By
Technicolor Security & Content Protection Laboratories
Technical Editor: Eric Diehl
Editors:
Sharon Ayalde
Contributors:
Patrice Auffret
Gwenaël Doerr
Alain Durand
Marc Eluard
Raphael Gelloz
Olivier Heen
Stéphane Onno
Yves Maetz
Xavier Rolland-Nevière
EVP:
CSO:
Gary Donnan
Rachel Orand
Subscribe to the newsletter:
security.newsletter(at)technicolor.com
Report vulnerability:
security(at)technicolor.com
EDITORIAL
Anti-piracy is starting this new year with good omen. The first event is the shutdown of
MegaUpload on January 19th. What a surprise! For many months, piracy due to illegal
streaming has exceeded piracy due to Peer-To-Peer (P2P). MegaUpload became the
flagship of cyber lockers, as “The Pirate Bay” is the flagship of P2P. With servers spread
all over the world and operators also disseminated, their position seemed impregnable.
Nevertheless, a US grand jury indicted seven individuals and two societies of engaging
in a racketeering conspiracy, conspiring to commit copyright infringement, conspiring
to commit money laundering and two substantive counts of criminal copyright
infringement. Following this decision, the FBI launched a vast operation ending up with
the arrest of four individuals in New Zealand who should soon be transferred to the US,
and the seizure of servers. Game over!
The second event is the voluntary shutdown of BTJunkie. Although not the largest
torrent tracker site, BTJunkie was one important piece of the P2P landscape (5th
position). Since the 14th of February, the site is closed. Are these two events related?
To the best of our knowledge, BTJunkie was not under any legal suit.
Will that stop piracy? Of course, the answer is no. Obviously, we will see a serious
slowdown of illegal download/streaming. As many people/promoting sites relied on
MegaUpload, some time will be necessary to seed new cyber lockers with illegal content
and to promote them. A successor to MegaUpload will most probably appear in the
coming months. Then, was that operation useless? No. The content owners have
demonstrated that there may be nothing such as an impregnable harbor for illegal
content trading. In addition to the immediate temporary impact on piracy, it may send
a strong, deterrent, pedagogical message to pirates (at least light-hearted ones).
A collateral effect may be that people will rethink the conditions to use free cloud
storage. MegaUpload was also used for legitimate content storage. All this information
is now lost as we may expect that their owner did not locally back it up. This is an
interesting topic to explore.
2012 may be a very thrilling year in the security and content protection arena.
E. DIEHL
Technical Editor
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BE OUR GUEST
Refik Molva
Hello Refik, may you introduce yourself?
I am a security researcher in computer and communication systems.
I am a full professor at EURECOM and in addition, I am in charge of
the Networking and Security Department at EURECOM.
How did you get into security?
This comes back to the time when security was not seen as a full
research topic. In 1989, I was a researcher at IBM Zurich and my main
topic was networking. With my first line manager, Phil Janson, we
wanted to explore a new domain so we visited other IBM labs in the
US. Network security just came up as evidence. I started working
with Moti Yung on the security of an authentication protocol and we
discovered and fixed vulnerabilities. This was a seminal work under
the codename KryptoKnight that paved the way for the outstanding
security activity in IBM Zurich. That was my first contribution to
network security, and I have never stopped since.
Three years later, I joined EURECOM as the only security
researcher. Security kept being marginal, even deemed a bit
suspicious, until the end of the nineties. With the advent of Internet,
everything changed up to a point that now security is considered as
an “easy” research topic since there is so much to do.
What are the main research domains that you have
explored?
You did not mention privacy yet…
Whenever a new communication or computing paradigm arises,
I try to spot original security problems raised by that paradigm.
For instance, when multicast was a popular topic in networking,
authentication of multicast flows was a brand new problem. Unlike
unicast that can be addressed by symmetric message authentication
techniques, multicast authentication inherently calls for asymmetric
mechanisms. Asymmetric algorithms on the other hand are way
too complex for real time traffic, so one had to come up with a new
solution, that’s what we did with Alain Pannetrat, my Ph.D. student
by then.
On that topic I want to stress that privacy does not only involve
confidentiality. Unlinkability or even unobservability are equally
important privacy requirements. Especially when applied to the
cloud this might lead to exciting challenges. As an example, I can
quote the apparent contradiction between cloud authentication and
unlinkability.
In the early days, my focus was very broad: applied cryptography,
network security, protocols. But at that time, one could still afford to
cover so many topics. From 2003, I focus on the design of security
protocols using cryptographic techniques.
Another example is with the advent of ad hoc networks that certainly
raise several security requirements but only very few actually called
for novel solutions. We were among the few research groups that
identified selfishness as a new problem in ad-hoc networks and
formalized it using game theory.
I was coming to this point. I currently investigate privacy problems in
relation with cloud computing. Straightforward application of classical
privacy mechanisms out of the crypto bag of tools is not sufficient:
the additional difficulty comes from the very distributed nature of the
cloud. Understanding this difficulty leads to new security protocol
designs like, for instance, PRISM (Privacy-Preserving searches in
Map-Reduce).
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Do you think there are unsolvable privacy issues?
No. At least not from a pure technology point of view. Problems
arise from our capacity to state the real needs. For instance, there is
an obvious contradiction between usages in social networks and the
privacy of both personal data and Personally Identifiable Information.
A related difficulty is that many users do not spontaneously
request privacy. This is counterbalanced by the recent European
directive. This makes administrations aware of privacy issues and
responsibilities, as well as organisms funding research, which I
personally find a very good thing. Along the same lines, we recently
were invited by public authorities to join a consortium in order to
investigate privacy issues in a project involving RFID tags. This
would have never happened without this directive.
Do you have any thought about research that you
would like to share?
I am concerned about the overall trend with increasing constraints
and short-term expectations from research. Research by definition
can barely yield any useful outcome if constrained by concrete
objectives. In the long run, betting on open-ended research will be
much more profitable. A noteworthy counter-example is European
R&D programs that claims to cover a broad range of goals from
fundamental research through prototyping and standardization to
business exploitation in projects within 2-3 year timeframe. Programs
and funding for fundamental research should be separate from the
ones for technology transfer and innovation, like NSF and DARPA
are in the US.
Thank you!
R. MOLVA (EURECOM, Sophia Antipolis)
Interview by A. DURAND and O. HEEN
THE NEWS
Patriot Act vs. Cloud?
Signed by President Bush shortly after the attacks of 9/11, the Patriot
Act aims at easing the gathering of data by American federal
agencies in order to fight terrorism. Many people understand:
“federal agencies may silently get data”. Some US cloud providers
view it as a competitive drawback as non-US customers may
perceive it as a threat for their data. While this might make sense at
a first glance, a deeper analysis shows this is not rational. Non-US,
security aware customers would actually classify data regarding
their sensitivity. Non-sensitive data may go in any cloud. Regionalsensitive data shall go in regional clouds (like EU data in EU provider
OVH, OBS, etc.) and sensitive data out-of-the-cloud anyway. In
this context, the Patriot Act1 is not really a problem. Since most
of the data is non-sensitive, the leading cloud provider will gather
the largest market share regardless of regional laws and security
context. This means the market is fairly competitive as long as the
main amount of data in the cloud is non-sensitive, which seems to be
currently the case.
O. HEEN
Ghost Click
First experiments on internet advertisement started in 1994.2 Many
monetizing strategies and tools are possible. Internet advertisement
has become a multi-billion-dollar industry and therefore a new target
of attacks.
Two years of collaboration between law enforcement and security
researchers led to the arrest of six men who were operating a large
fraud on internet advertisement: they are suspected to have made
$14 million through click-jacking.3
The attack targeted the click referral principle where a host receives
a small fee for redirecting a client to an advertised website. The
attacker used a Domain Name System (DNS) changer malware. The
malware redirected heavy traffic like iTunes or Netflix to other sites
where they had advertisement agreements.
Four million computers are infected and their users may not be
aware. The rogue DNS servers have been shutdown, and replaced
by legitimate ones. Thus, the infected computers can still access
the Internet. These servers will be operated until this spring, leaving
some time for deceived users to detect the infection and correct
their DNS settings.
1 ‘The USA PATRIOT Act: Preserving Life and Liberty’, http://www.
justice.gov/archive/ll/highlights.htm.
2 Rachel Arandilla, ‘Rise and Fall of Online Advertising’, 1st Web Designer,
March 1, 2011, http://www.1stwebdesigner.com/design/online-advertisinghistory/.
3 ‘International Cyber Ring That Infected Millions of Computers
Dismantled’, FBI, http://www.fbi.gov/news/stories/2011/november/malware_110911/malware_110911.
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If this type of attacks spreads, it could deeply impair the Internet
advertisement industry. These attacks exist in many forms, even lowtech ones. For instance, an attack may use human computing power,
such as Amazon’s mechanical Turk4, to improve referring fees, or to
increase the advertisement cost of a competitor…
Y. MAETZ
TOR and the Pedophiles
In October 2011, members of the Anonymous hacktivist collective
claimed to have shut down more than 40 child pornography websites
hidden inside the Tor network. The account details of about 1,600
members of Lolita City, the largest of those websites, were posted
as well.5 This takedown was part of a larger ongoing anti-child
pornography campaign launched by Anonymous called Operation
Darknet (#OpDarknet).6,7
The Tor network provides anonymity to individuals seeking to evade
government surveillance or censorship but is also used to conceal
various illegal activities. Tor not only provides anonymization for
clients but also for websites through the unofficial DNS top-level
domain (TLD) .onion.8 Such websites are only reachable by Tor
clients or through Tor gateways such as tor2web.org.9 This collection
of onion websites constitutes what is called a darknet. Darknet
websites are part of the Invisible Web (or Deep Web), a part of the
Internet not indexed by standard search engines. In the case of Tor,
there is a Hidden Wiki that indexes hundreds of these onion sites.
However, onion websites are still vulnerable to conventional attacks
such as the ones launched by Anonymous.
While browsing the Hidden Wiki, members of Anonymous
discovered a section called Hard Candy linking to child pornography.
Anonymous removed the links but they were quickly reposted.
Having determined that the hosting platform Freedom Hosting was
used to store most of this content, the hacktivists unsuccessfully
requested the removal of this content. Consequently, Anonymous
launched denial of service attacks against Lolita City and Freedom
Host. The hackers were also able to fetch Lolita City’s accounts
information by using a UTF-16 encoded SQL injection.
Unsatisfied by these results and the lack of involvement of
authorities, Anonymous pursued OpDarknet by disclosing the IP
address of 190 consumers of child pornography (“Operation Paw
Printing”). The hackers created a Firefox plugin similar to the Tor
Button (“The Honey Pawt”) and tricked the Hard Candy users
4 ‘Amazon Mechanical Turk - Welcome’, https://www.mturk.com/mturk/
welcome.
5 #OpDarknet, ‘Lolita City user dump’, Pastebin, October 18, 2011, http://
pastebin.com/88Lzs1XR.
6 ‘Operation DarkNet (opdarknet)’, Twitter, https://twitter.com/
opdarknet.
7 ‘Opdarknet’s Pastebin’, Pastebin, http://pastebin.com/u/opdarknet.
8 Chen Adrian, ‘The Underground Website Where You Can Buy Any
Drug Imaginable’, Gawker, June 1, 2011, http://gawker.com/5805928/the-underground-website-where-you-can-buy-any-drug-imaginable.
9 ‘Tor: Hidden Service Protocol’, Tor project, https://www.torproject.org/
docs/hidden-services.html.en.
to install it under the guise of a phony Tor security update. Once
installed this plugin blocks and redirects all traffic going to child
abuse websites back to Anonymous’ IP logger.
Many found such actions justified by the perceived lack of efficiency
of the authorities whose investigations take years before bringing
a few pedophiles to justice. However, Anonymous does not have
the resources to track the criminals down and bring them to
justice. In this case, they tipped off criminals who could destroy the
evidence and learned to be more cautious. OpDarknet also possibly
compromised an ongoing investigation by tampering (or being
accused of tampering) with evidence.
R. GELLOZ
XML Encryption is eXtreMeLy Weak
The XML Encryption scheme defined by the W3C seems to be
quite straightforward: They use the well-known CBC (Cipher Block
Chaining) mode with random IVs (Initialization Vectors) and a basic
padding scheme consisting in adding (possibly zero) random bytes
and then the last byte giving the total number of bytes that were
padded. Nothing is very original and, therefore, the encryption
scheme looks to be basically secure. However, two researchers of
the Bochum University (Germany) have shown10 that it is possible
to recover the clear text from an encrypted text (but not the
key) as long as the attacker has access to a device (called oracle)
able to determine whether a given ciphertext (and associate IV)
corresponds to a well-formed cipher text for a given, unknown key.
Here, well-formed means that the clear text was correctly padded
and only has XML characters (more or less standard text characters).
Their attack heavily relies on one property of the CBC mode: When
used to decrypt, a modification by some mask of one byte in the IV
(or at a given cipher block) causes a modification by the same mask
in the first decrypted block (or at the block following the modified
block). Based on that property and on the responses from the oracle
(i.e. well-formed or bad-formed), it is possible to recover the full plain
text in reasonable time.
This new attack shows again that the security of cryptographic
primitives heavily depends on the context: a perfectly secure
cryptographic primitive – like CBC – may be attacked if used in a
bad context (only XML characters may be encrypted). The attack is
another reminder11 that it is generally bad for an implementation of
a cryptographic primitive to be too talkative by revealing that some
error occurred.
A. DURAND
10 Tibor Jager and Somorovsky Juraj, ‘How to break XML encryption’,
in Proceedings of the 18th ACM conference on Computer and communications security, CCS ’11 (New York, NY, USA: ACM, 2011), 413–422,
doi:10.1145/2046707.2046756, http://doi.acm.org/10.1145/2046707.2046756.
11 Daniel Bleichenbacher, ‘Chosen ciphertext attacks against protocols based on the RSA encryption standard PKCS #1’, in Advances in
Cryptology — CRYPTO ’98, ed. Hugo Krawczyk, vol. 1462 (Berlin/
Heidelberg: Springer-Verlag), 1–12, http://www.springerlink.com/content/
j5758n240017h867/.
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Duqu: yet another Stuxnet?
Duqu malware was discovered in September of 2011. The name
Duqu comes from the ~DQxxx.tmp files it drops, where xxx is a
hexadecimal number. Duqu infects hosts, and gathers sensitive
information like keystrokes, digital certificates, etc. New malware
regularly appears but Duqu raised interest because it was technically
close to Stuxnet malware. Stuxnet was discovered in June of 2010
and was very innovative at least regarding its target, an industrial
system, and the high number of vulnerabilities used. Modifying an
existing malware (Stuxnet) for making a new malware (Duqu) is a
common practice. Even if Stuxnet and Duqu have different targets,
their specificity raises some questions: do they come from the same
“lab” or even from the same “toolkit”. Shall we expect other worms
from the same “family”?
O. HEEN
WPS:
THE NEW WEP?
Wi-Fi Protected Setup (WPS) is a standard designed by the Wi-Fi
Alliance (WFA) for easy connection of Wi-Fi enabled devices.
The goal is to make it easy, without sacrificing the security offered
by a Wi-Fi protection mechanism such as Wi-Fi Protected Access
(WPA) for instance. In this article, we will describe the vulnerability as
disclosed by Viehboeck12 that shows security has been sacrificed for
usability.
WPS works in three scenarios: Push Button Configuration (PBC),
Internal Registrar (IR), and External Registrar (ER). PBC mode
requires the end-user to push a button prior WPS authentication can
work. IR mode also requires an end-user interaction via its own client
stations (STA). ER mode does not require end-user interaction.
half). By considering that a PIN code attempt takes two seconds to
complete, the full range of possibilities can be tested in around six
hours. With statistical theory, this will be half that time: three hours.
100,000,000 COMBINATIONS
1
1
2
2
3
3
4
4
10,000
COMBINATIONS
5
6
7
5
6
7
8
8
1,000
COMBINATIONS
Viehbock’s paper is called “When poor design meets poor
implementation”. Our view is that the design is not the only guilty
here. WFA decided to build a protocol which recovers a wireless key,
regardless of its size, by using an 8-digit PIN code, i.e. less than 27
bits. Reducing the key space to make it easier for the end-user is a
lousy idea.
In the title, we compared WPS to Wired Equivalent Privacy (WEP).
We did that on purpose, because before the WPS discovery, pirates
were trying to crack the easy WEP. Now, WPS is an even easier
attack. Today, the best tool to break WPS is Reaver.13
It is time for WFA to rethink security. WFA has just recommended
a permanent lockout after 10 failed attempts. Of course, this lockout
could be reset by rebooting the device.
P. AUFFRET
PS: Technicolor DSL gateways implemented an anti-brute force
mechanism before this vulnerability was discovered. There is a five
minutes lockout after five failed attempts.
The disclosed attack is against ER mode. Because there is no enduser interaction, an attacker can launch a brute-force attack to find
the correct Personal Identity Number (PIN) code. The vulnerability
comes from a bad design in the authentication protocol, and a lack of
anti-brute force requirements in the specification. Thus, every device
implementing WPS mode is vulnerable.
The authentication algorithm uses an 8-digit PIN code. The
validation of the PIN code is a two-step process: each one separately
validating one-half of the PIN. When the first half (4-digit) is valid,
the access point answers with a specific message. When the second
half (3-digit) is valid, a different message is answered. Finally, the
last digit is a checksum of the seven previous digits. Consequently,
the number of possible attempts is reduced from 100,000,000 to
11,000 (10,000 for the first half followed by only 1,000 for the second
12 Stephan Viehböck, ‘Wi-Fi Protected Setup PIN brute force vulnerability’, .braindump - RE and stuff, December 27, 2011, http://sviehb.wordpress.
com/2011/12/27/wi-fi-protected-setup-pin-brute-force-vulnerability/.
13 ‘reaver-wps - Brute force attack against Wifi Protected Setup - Google
Project Hosting’, http://code.google.com/p/reaver-wps/.
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CARRIER IQ
Carrier IQ is a California based company founded in Mountain View
in 2005. Carrier IQ’s mission is to provide embedded analytics for
mobile phones to telecom carriers. The goal is to improve “users
experience” by gathering information when phone calls are dropped,
where signal quality is poor, why applications crash, and which
processes drain the battery. For instance, it could help carriers to
know if additional cells are required when too many calls are dropped
in a particular location. Trevor Eckhart, a system administrator,
pointed out a Carrier IQ patent14 that claimed a broader scope:
“Carrier IQ is able to query any metric from a device. A metric can be
a dropped call because of lack of service. The scope of the word metric
is very broad though, including device type, such as manufacturer and
model, available memory and battery life, the type of applications
resident on the device, the geographical location of the device, the
end user’s pressing of keys on the device, usage history of the device,
including those that characterize a user’s interaction with a device”.
After having reversed–engineered the Carrier IQ software, the truth
appeared to be different on two key points. First, the Carrier IQ
software acts as a rootkit15 as it runs hidden from users and requires
the device to be rooted to uninstall it. Uninstalling requires a strong
technical background and often voids the phone’s warranty. Second,
the rootkit acts as a spyware since it may send every user activity to
carriers.
Carrier IQ software is suspected to be installed over more than 150
million phones from Android, Windows, BlackBerry, and even on
iPhones on which it is off by default. Although Carrier IQ declined
to disclose their customers, T-Mobile, Sprint, AT&T, Samsung, and
HTC acknowledged having installed the carrier IQ software and they
disclosed the list of affected phones.20
A Carrier IQ lawsuit class action21 has been submitted. By illegally
collecting user information, Carrier IQ probably violated the Federal
Wiretap Act. More recently, US representative Edward Markey
proposed a mobile device privacy act to force carriers and phone
makers to inform consumers about spying software that could only
be installed with an expressed consent of the consumer.
Similarly to Sony BMG22 in 2005, telecom carriers installed rootkits
on consumer devices without consumers’ authorization. Each time a
company acted like that, it led to alter its brand image and damage
its consumer’s trust relationship. Such privacy leakage on consumer
products is probably the visible tip of the iceberg. Most data is now
managed and kept on the cloud by operators, making such leaks hard
to detect in practice. How can consumers know which information is
collected? Which information leaks?
Is our digital world still compatible with privacy?
S. ONNO
Trevor reverse-engineered the Carrier IQ software. In September
2011, he disclosed his first results at XDA developer forum.16 He
revealed that the software collects sensitive information despite the
user never “opting-in”. On November 28th, he posted a video on
YouTube17, which shows results from his Android application – namely
Logging TestApp - launched on a HTC (Evo 3D)/Sprint mobile
phone with detailed application logs and results.18 Beyond data that
Carrier IQ admitted collecting, Trevor also observed additional data
such as pressed keys and trigger actions, SMS in clear text, URLs
dispatched to the carrier, and even HTTPS content intercepted and
revealed in the clear. Even more, if the user resigns from his carrier
and only uses a Wi-Fi connection, the phone continued to report
carrier IQ data. These Carrier IQ profiles list which data are sent,
for whom and when. On December 2011, the Electronic Frontier
Foundation launched an initiative to collect and analyze the Carrier
IQ profiles after Jered Wierzbicki and Al. published a tool to convert
Carrier IQ profile to human-readable XML.19
14 Steve Roskowski et al., ‘Data collection associated with components
and services of a wireless communication network’.
15 ‘Rootkit’, Wikipedia, the free encyclopedia, http://en.wikipedia.org/
wiki/Rootkit.
16 TrevE, ‘Were you aware of logging before reading news? -’, xda-developers, September 7, 2011, http://forum.xda-developers.com/showthread.
php?t=1252846.
17 Carrier IQ Part #2, 2011, http://www.youtube.com/watch?v=T17XQI_
AYNo&feature=youtube_gdata_player.
18 Trevor Eckhart, ‘CarrierIQ Part 2’, http://androidsecuritytest.com/
features/logs-and-services/loggers/carrieriq/carrieriq-part2/.
19 Dan Rosenberg, ‘Unpacking Compressed Carrier IQ Profiles’, It’s
Bugs All the Way Down, December 25, 2011, http://vulnfactory.org/
blog/2011/12/25/unpacking-compressed-carrier-iq-profiles/.
20 FiWeBelize, ‘The Complete List of All the Phones With Carrier
IQ Spyware Installed’, FiWeBelize, January 18, 2012, http://fiwebelize.
com/2012/01/18/the-complete-list-of-all-the-phones-with-carrier-iqspyware-installed/.
21 ‘Carrier IQ Class Action Lawsuit’, http://www.carrieriqclassactionlawsuit.com/.
22 Mark Russinovich, ‘Sony, Rootkits and Digital Rights Management
Gone Too Far’, Mark’s Blog, October 31, 2005, http://blogs.technet.com/
markrussinovich/archive/2005/10/31/sony-rootkits-and-digital-rights-management-gone-too-far.aspx.
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PROTECTING
COMPUTER
GENERATED 3D
GRAPHICS
Computer generated graphics have been steadily invading our daily
lives over the last decade. Today, virtual objects are routinely used in
a number of applications, including socializing metaverses, games,
simulation tools, and user interfaces. Incorporating visual effects into
movies is now considered the norm in the entertainment industry.
Blockbuster animation movies inherently rely on synthesizing rich and
complex 3D worlds.
Creating these virtual objects/environments is a lengthy artistic
process and the resulting end-product does have value. Losing
control over the dissemination of such assets may lead to a number
of uncomfortable situations for major Hollywood studios:
•
Unfinished 3D assets that hit the Internet can generate bad
press for the studio that created it;
•
3D components that somehow leaked out of the content
production workflow can be reused in other uncontrolled virtual
context, potentially harming royalties revenues;
•
3D objects, which are available to the public, could be exploited
as a template to manufacture derivative products, e.g. by using
modern 3D printers.
In view of this situation, it is reasonable to envision a content
protection framework for computer generated 3D graphics and
thus to review whether existing technologies could be adapted in a
straightforward fashion or not.
Content protection architectures conventionally include three main
components. First, encryption techniques are applied to digital data
in order to make it unusable to parties that do not hold the necessary
credentials. Second, an access control framework is set in place to
(i) attach usage rules to digital items in the form of a ticket/license/
permit, (ii) assign credentials to the individuals, and (iii) enforce the
rules of the ecosystem. Third, a forensic strategy is deployed in order
to be able to locate the source of a leak in the event that copyrighted
assets were to appear on unauthorized distribution networks.
This final component typically relies on the combination of digital
watermarking technology and traitor tracing codes.
Although it may be necessary to enrich existing rights expression
languages to accommodate for the specificities of the 3D graphics
ecosystem, it will a priori be possible to readily reuse standard
baseline access control framework routinely employed in current
Digital Rights Management (DRM) systems. In contrast, encryption
and watermarking techniques need to be revisited to better fit
the particular needs of 3D data. The remainder of the article will
therefore focus on these two elements.
Scrambling 3D Objects
From the perspective of a cipher, bits are simply bits. It does not
matter if those bits represent audio, video, or 3D graphics. They
will eventually be ciphered in the same way. As a result, the most
straightforward strategy to encrypt a 3D object is simply to bulk
encrypt the binary file that describes it.
The issue with such a direct approach is that the underlying structure
of the file23 is also lost at encryption time. As a result, rendering
engines oblivious to encryption will fail to parse the file which may
lead to a critical crash of the attached player. A workaround consists
in preserving the structure of the 3D file and to bulk-encrypt the
structural elements independently. This is typically the solution
adopted for XML encryption.24 This revised strategy also provides a
finer granularity e.g., one could decide to only encrypt a few objects
in a complex 3D scene.
Nonetheless, major shortcomings remain. On the one hand, a
renderer that does not have the necessary credentials to decrypt
data may display nothing at the location of the protected object.
Depending on the targeted use case, it may not be the ideal solution.
In a metaverse, if the user has no means to notice that ‘something’ is
missing, it is unlikely that she will miss anything. In other words, the
incentive to pay extra money to have access to enriched premium
environments is lacking. In an animation production workflow,
the studio may not be willing to give access to the full scene to a
subcontractor. However, this subcontractor should be able to have
access to visual cues in order to insert graphical elements without
hitting existing ones that would be invisible due to the protection
framework. On the other hand, if the renderer attempts displaying a
protected object based on its encrypted data, the object will ‘spill out’
and corrupt the whole 3D scene.
Geometry Preserving Encryption
In cryptography, the terminology ‘format preserving encryption’
refers to particular ciphers that have the property of producing
an output (the ciphertext) in the same format as the input (the
plaintext).25 Preserving format may mean preserving some geometric
23 A number of formats have been proposed to describe 3D scenes.
They all rely on some kind of structure to separate different types of data.
For instance, in the most simple case, a 3D object is composed of a set of
vertices with 3D coordinates (aka. the geometry) and a set of faces obtained
by connected the previously defined vertices with edges (aka. the connectivity or the topology). The structure of the file can be implicit through the
use of a particular convention, or explicit with XML-like tags to separate the
different pieces of data.
24 ‘XML Encryption Syntax and Processing’ (W3C, December 10, 2002),
http://www.w3.org/TR/xmlenc-core/.
25 Mihir Bellare et al., ‘Format-Preserving Encryption’, in Selected Areas
in Cryptography, ed. Michael J. Jacobson, Vincent Rijmen, and Reihaneh
Safavi-Naini, vol. 5867 (Berlin, Heidelberg: Springer Berlin Heidelberg,
2009), 295–312, http://www.springerlink.com/content/g4u222r2355h7536/.
9
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#21
Figure 1: Illustration of the visual impact of two alternate geometry preserving encryption techniques
(PointShuffling vs. CoordShuffling) as well as their immunity against reconstructions attacks.
Assessing the security of geometry preserving encryption techniques will For
be instance,
critical. if security properties of the protected object.
anEven encrypted
object
requirements are not the same for the entertainment should remain within the bounding box (or the convex hull, or the
inside,the or…) military of the original
object. A by-product
of such aof geometry
and industries, the security a newly preserving encryption would be that both protected and unprotected
proposed cipher still needs to be properly evaluated object could be rendered in a virtual world without any undesired
and should between
be in nthe
o wobjects.
ay reduced to the security of the interferences
underlying cryptographic primitive. Multimedia items For illustrative purpose, let us consider a simple 3D object defined
have lot of redundancy partial of the by a seta of
V vertices
v i=(xi , yi , zand ) and
a set ofeFncryption faces, each face
i
being
specified
by
an
ordered
list
of
the
vertices
that
it
is
made
of.
data may not be enough to guarantee security, even if In other words, the first component defines the control points of
the content is unintelligible when it is rendered. the 3D mesh whereas the second one describes how the mesh is
Important information may still leak that would wired altogether.
A simple geometry
preserving
scrambling
strategy,
permit the protected object to some hereafter reconstructing referred to as PointShuffling,
consists
in shuffling
the
set
of
vertices
of
the
3D
object
i.e.
applying
a
key-seeded
pseudo
extent e.g. exploiting a prior on the statistics of the random permutation σK26
( ) to the indices i of the vertices v . The
underlying signal. This is a lesson learnt i the hard impact of this process is to change the set of vertices associated
way in selective ncryption or multimedia content. to a given
face while e
leaving
the 3D fcoordinates
of the vertices
untouched. Inherently, it is equivalent to pseudo-randomly rewiring
Looking back defining
at the PointShuffling scrambling the cloud of points
the object.
As a result, if the protected
object
is
rendered,
it
is
unintelligible
(as
depicted
in
Figure
1) even
algorithm, it is actually possible to recover quite a if it does not spill outside of the convex hull of the original object.
decent looking 3D object. As mentioned earlier, the When having access to the secret key K, recovering the original
object
is then
simply
a matter
of applying
the
inverse permutation to
the set of vertices of the protected object.
definition of the vertices themselves has not been modified i.e. their positions in the 3D space remained protected
object to some extent e.g. exploiting a prior on the
26
unchanged. As a signal.
result, if is the rendering engine statistics
of the underlying
This
a lesson
learnt the hard
way
in
selective
encryption
for
multimedia
content.
ignores the face information and simply displays the Looking
back
the PointShuffling
scrambling
algorithm,
vertices of atthe object, the resulting cloud of points itprovides a lot of information about the original shape is actually possible to recover quite a decent looking 3D object.
As mentioned earlier, the definition of the vertices themselves has
of the object as depicted in Figure 1. At this stage, the not been modified i.e. their positions in the 3D space remained
recovery As
process then a simple applying unchanged.
a result, ifis the
rendering
engine matter ignores theof face
information
and
simply
displays
the
vertices
of
the
object,
the
conventional surface reconstruction techniques.27 The resulting cloud of points provides a lot of information about the
resulting reconstructed object will be very close to the original shape of the object as depicted in Figure 1. At this stage, the
original process
one, iseven it is nmatter
ot exactly the conventional
same. A quick recovery
then aif simple
of applying
27
surface
techniques.
The
resulting is reconstructed
fix to reconstruction
address this security problem to apply three object
will bepermutations very close to the original
one,individual even if it is not
exactly
different to the coordinates the same. A quick fix to address this security problem is to apply
of the vertices: three different permutations to the individual coordinates of the
vertices:
xi ← xσ K (i )
x
yi ← yσ K (i )
y
CoordShuffling: zi ← zσ K z (i )
CoordShuffling: . This creates new vertices, which are in the bounding box of the
original object, hence granting the desired geometry preserving
property.
As
depicted
in
Figure
1, this
protected
object is
unintelligible
and
the
reconstruction
attack
is now ineffective.
26
Amir Said, ‘Measuring the strength of partial encryption schemes’, in IEEE International Conference on Image Processing, 2005. ICIP 2005, vol. 2 (presented at the IEEE International Conference on Image Processing, 2005. ICIP 2005, IEEE, 2005), II– 1126–9, doi:10.1109/ICIP.2005.1530258. 27
Tony Derose et al., Surface reconstruction from unorganized points (CiteSeerX, 1992), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.47
35. Figure 1: Illustration of the visual impact of two alternate geometry preserving encryption techniques
(PointShuffling vs. CoordShuffling) as well as their immunity against reconstructions attacks.
Assessing the security of geometry preserving encryption techniques
will be critical. Even if security requirements are not the same for
the entertainment and the military industries, the security of a newly
proposed cipher still needs to be properly evaluated and should be
in no way reduced to the security of the underlying cryptographic
primitive. Multimedia items have a lot of redundancy and partial
encryption of the data may not be enough to guarantee security,
even if the content is unintelligible when it is rendered. Important
information may still leak that would permit reconstructing the
Still, the surface reconstruction attack is only one out of many
yet unidentified threats against geometry preserving encryption
schemes. 3D objects provide many side information channels that
can be exploited by an adversary. For instance, should our basic
26 Amir Said, ‘Measuring the strength of partial encryption schemes’, in
IEEE International Conference on Image Processing, 2005. ICIP 2005, vol.
2 (presented at the IEEE International Conference on Image Processing,
2005. ICIP 2005, IEEE, 2005), II– 1126–9, doi:10.1109/ICIP.2005.1530258.
27 Tony Derose et al., Surface reconstruction from unorganized points (CiteSeerX, 1992), http://citeseerx.ist.psu.edu/viewdoc/
summary?doi=10.1.1.153.4735.
10
THE SECURITY
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3D mesh be enriched with a texture28, there is a strong correlation
between (i) the distance between 3D vertices and (ii) the distance
between the corresponding 2D points in the texture image.
Scrambling a single one of these two elements leaves the door
open to a potential reconstruction attack. On another front, the
connectivity of the object provides a lot of information on the actual
geometry of the mesh and can thus helps to reconstruct an object.29
Security evaluation relying on the identification of potential threats
due to information leakage will be one of the most sensitive tasks
when designing novel geometry preserving ciphers for 3D graphics
and should therefore not be overlooked.
Challenges Beyond Security
Keeping in mind that such protection technologies for 3D graphics
will need to be eventually integrated into a content production and/
or distribution workflow, a number of non-security related challenges
arise.
Let us examine the question of distribution of 3D objects to begin
with. In many application scenarios, the 3D mesh is streamed to the
recipient for interactive visualization e.g. for 3D games or metaverses.
To save bandwidth, a routine trick consists in partitioning the data
in bags of connected faces and sending a bag of faces only if it
contains an element facing the virtual camera.30 In most cases,
the whole object will not be received by the recipient but only the
components that might be visible in the scene. If this mechanism has
not been accounted for by the protection technique, the recipient
will not be able in most cases to recover the original object, even
if she possesses the corresponding credentials. For instance, with
both PointShuffling and CoordShuffling algorithms, a
single missing protected vertex results in being unable to invert the
pseudo-random permutation(s). To support this use case, it would be
necessary to protect each element of the partition independently, at
the risk of decreasing security at a critical level when the granularity
of the partition becomes too small. Even if this trade-off was to
be appropriately tackled, there would still be a remaining issue. In
general, the encryption techniques proposed so far have a strong
impact on the orientation of the faces. As a result, in each bag of
protected faces, it is very likely that there will be at least one element
facing the virtual camera. As a result, the whole object is streamed
to the receiver, hence losing the expected benefit in terms of
bandwidth.
Compression performances will also be affected if no care is taken
28 Adding a texture to a 3D mesh is simply a matter of providing a 2D
image and specifying where it is attached to the vertices defining the 3D
object. The texture image is then stretched and shrunk as necessary in order
to wrap it over the 3D faces accordingly.
29 M. Isenburg, S. Gumhold, and C. Gotsman, ‘Connectivity shapes’,
in Visualization, 2001. VIS ’01. Proceedings (presented at the Visualization, 2001. VIS ’01. Proceedings, IEEE, 2001), 135–552, doi:10.1109/
VISUAL.2001.964504.
30 Sheng Yang, Chang-Su Kim, and C. -C.J Kuo, ‘A progressive view-dependent technique for interactive 3-D mesh transmission’, IEEE Transactions
on Circuits and Systems for Video Technology 14, no. 11 (November 2004):
1249– 1264, doi:10.1109/TCSVT.2004.835153.
while integrating encryption techniques into a workflow. For instance,
some 3D compression techniques rely on the same prediction
framework as in audio and video.31 Based on some statistical prior
about 3D data, the encoder makes an informed prediction and only
entropy encodes the residual error. For instance, one could represent
a 3D mesh like an arrangement of baseline patches. In this case, the
encoder simply needs to send (i) a collection of baseline patches,
(ii) some geometric parameters (translation, rotation, scaling) to
position each patch, and (iii) the residual error for each patch in order
for the decoder to recover the object. The issue with encryption is
that it essentially produces a protected object that deviates from
the statistical a priori model underpinning the prediction strategy.
This subsequently results in unexpected residual errors that are
poorly compressed by otherwise optimized entropy encoders. The
compression ratio for protected object is bound to be significantly
lower than for original objects. A possible avenue could reside in
incorporating the cryptographic components within the encoding
architecture e.g. by protecting the parameters obtained after
prediction rather than the raw 3D mesh.
The final issue with geometry preserving encryption is related to
rendering, namely the impact of the protection on the number of
frames being displayed per second (the frame rate). To display a
3D scene, the rendering engine goes through a process referred to
as rasterisation. It essentially consists in identifying the position of
the faces in the video frame captured by the camera, and drawing
the resulting transformed faces when they are not occluded. To
know whether a new incoming face is occluded, the rendering
engine maintains a z-buffer which records for each pixel the
depth of the currently drawn face. If for a given image pixel, the
distance of the incoming face is smaller than the one currently
registered in the z-buffer, the considered pixel is updated using
the current face, as well as the z-buffer. Otherwise, it is discarded.
A geometry preserving encryption technique, on its side, distorts
the original object into something that may no longer look like a
regular 3D mesh. For instance, the protected objects output by the
PointShuffling and CoordShuffling algorithms are made
of faces that are in average much larger than the ones of their original
counterparts. Displaying such objects will therefore require many
more updates of the z-buffer than usually needed. As a result, user
experience may be degraded. Users with proper credentials will
have no penalty whereas users without credentials may get a slower
frame rate.
31 Jingliang Peng, Chang-Su Kim, and C.-C. Jay Kuo, ‘Technologies for
3D mesh compression: A survey’, Journal of Visual Communication and
Image Representation 16, no. 6 (December 2005): 688–733, doi:10.1016/j.
jvcir.2005.03.001, http://www.sciencedirect.com/science/article/pii/
S1047320305000295.
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Watermarking 3D Objects
Digital watermarking complements encryption and acts as a
deterrent against unauthorized uses of copyrighted content.
Essentially, watermarking consists in modifying multimedia content in
an imperceptible manner in order to convey additional information in
a robust fashion. The selling point of watermarking is that it survives
the D/A-A/D conversion (the analog hole). To be understood by
a human, multimedia content needs to be eventually decrypted.
At this very moment, the
content is left unprotected
and may be subject to
piracy attacks. In contrast, a
watermark is inherently tied
to the host signal and travels
along with it. Relying on this
property, one could exploit
watermarking to serialize
content at distribution time
i.e. each recipient receives a
slightly different version of
an asset, each one carrying
a unique watermark. As a
result, should a copy be
illegally redistributed, the
underlying watermark would
allow to pinpoint the source
of the leak.
There are mainly two communications systems routinely used as
the baseline for digital watermarking, namely spread spectrum
watermarking and watermarking with side-information (aka. dirty
paper watermarking).32 A content adaptation layer, which is in
charge of accommodating for the particular characteristics of the
host media, is then wrapped around this basis to obtain a full fledge
watermarking system. While the watermarking layer is relatively
mature and stable nowadays, there is still lively research activity to
devise better content adaptation layers, especially for exotic types of
media, such as 3D (animated) meshes, that only received marginal
interest so far.
Fidelity Metrics for 3D Content
One of the requirements of watermarking is for the introduced
modifications to remain imperceptible to a human observer. In the
context of 3D mesh, it immediately raises the key question: how do
you objectively measure the distortion between two 3D objects?
Shall it relate to the geometric distortion of the mesh in the 3D
space; or to the visual distortion that results after rendering from a
number of viewpoints; or to the haptic sensation felt after 3D printing
the objects? Fidelity metrics play a major part in watermarking and
should be properly defined. They are indeed exploited to assess
32 Ingemar Cox et al., Digital Watermarking and Steganography, 2nd ed.,
(The Morgan Kaufmann Series in Multimedia Information and Systems)
(Morgan Kaufmann, 2007).
the distortion introduced by the watermarking process as well as to
perceptually shape the embedded watermark in order to make it less
perceptible, e.g. by amplifying the watermark signal in regions where
it will be less perceptible and attenuating it elsewhere.
In 3D graphics, finding an appropriate objective distortion metric is
still an open issue.33 The most intuitive and straightforward approach
is the well-known Mean Squared Error (MSE), that aggregates the
Euclidean distance between pairs of vertices, one vertex being taken
from the original 3D mesh and the other being its watermarked
counterpart. Another popular
geometric distortion metric is
the (asymmetric) Hausdorff
distance, which is defined as
the largest Euclidean distance
from one surface to the other,
as well as its symmetric version
and numerous variants.
Nevertheless, experimental
results clearly showed that
these quantitative geometric
distortion metrics only exhibit
marginal correlation with the
perceptual distortion actually
perceived by a human being.
Similarly to what happened in
audio and video, this situation
motivated the definition of new metrics incorporating perceptual
principles, rather than focusing solely on geometric information.
Watermarking can indeed be assimilated to noise addition, which
translates as the insertion of a rough component on the surface using
3D terminology. Since the introduction of roughness on a smooth
surface is very noticeable, one could weigh the distortion measured
locally on the surface of a 3D object with the roughness at this
location in an attempt to better approach human perception.
A major downside still remains: these metrics are usually defined for
rigid 3D meshes and might not be fit to capture the distortion for
animated meshes. A small distortion considered as imperceptible in
a given pose may well be much more noticeable in another pose of
a dynamic mesh. For instance, a small modification at the tip of the
elbow may not have the same visibility depending on if the arm is
contracted or extended. An early work has recently been published to
tackle this issue34 but fails to fully solve the problem. It provides means
to measure distortion when considering a precise animation made
of well identified poses but does not succeed to define a universal
metric that would grasp the distortion for any animation that could
be generated from a rigged 3D mesh (a ‘rigged’ 3D mesh contains a
skeleton which can be used to define a wide range of poses).
33 Abdullah Bulbul et al., ‘Assessing Visual Quality of 3D Polygonal
Models’, IEEE Signal Processing Magazine, November 2011.
34 L. Vasa and V. Skala, ‘A Perception Correlated Comparison Method
for Dynamic Meshes’, IEEE Transactions on Visualization and Computer
Graphics 17, no. 2 (February 2011): 220–230, doi:10.1109/TVCG.2010.38.
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Manipulating 3D Objects
Fidelity metrics are also used to specify a distortion range within
which watermarks embedded in 3D objects are expected to be
retrieved successfully. The robustness of a watermarking system
is indeed evaluated with respect to the ability of the watermark
signal to survive even if the protected asset is further manipulated.
Obviously, severe degradation of the content will make the detector
miss the watermark and this is the reason why robustness is limited
to attacks35 that preserve the usability of the content, said usability
being typically defined in terms of distortion.
For conventional media (audio, image, video), the watermarking
community partitions attacks into two main categories. Synchronous
attacks encompasses all signal processing primitives that modify the
samples value of the signal e.g. noise addition, filtering, compression,
etc. In contrast, desynchronization attacks refer to signal processing
operations that modify the samples position and thus disrupt the
implicit alignment shared between the watermark embedder and
detector36. This well-established classification is however quite
ill-fitted for 3D content since nearly all 3D operations modify the
position of the vertices.
In computer graphics, the distinction is rather made between
operations that affects the geometry only (i.e. the position of the
vertices) and the primitives that may also impact the topology
(i.e. the connectivity between vertices). The first category covers
standard signal processing operations such as noise addition,
quantization, and surface smoothing, as well as basic similarity
transformations such as rotation, scaling and translation. It is really
with attacks on the topology that the specificities of 3D data is
fully revealed. A 3D mesh can be indeed seen as a particular nonuniform sampling of a virtual 2D surface in a 3D space. From this
perspective, the mesh can be affected by a number of operations
that for instance intends (i) to enrich the mesh with new vertices to
refine the approximation of the surface, (ii) to simplify the mesh by
discarding vertices while preserving the approximation of the surface,
or even (iii) to define a brand new sampling of the surface. There is
no equivalent to such attacks in conventional media such as audio,
image, and video.
This already large spectrum of potentially watermark-harming
operations is further widened when this review is no longer restricted
to rigid objects and is further extended to animated 3D meshes.
For instance, a skeleton-based animated object can be defined by
a regular 3D mesh that is attached to a skeleton made of ‘bones’
hierarchically connected through joints, each of these joints having a
number of degrees of freedom (constructing such a skeleton is called
35 Signal processing operations applied to the content after watermarking
are routinely referred to as ‘attacks’, regardless of the intent of the person
(regular casual use vs. hostile adversary) performing the operation.
36 Digital watermarking is a communications channel and as such relies
on implicit conventions between the emitter and the receiver (length of the
transmitted symbols, symbols codebook, synchronization). If the synchronization between the two parties is perturbed, the watermark message may no
longer be retrieved even if it remains present in the host data in a somehow
latent state.
the ‘rigging’ of the object). The animation then consists in applying a
transformation to the skeleton that naturally cascades to the ‘skin’. As
a result, from a single animated 3D mesh, it is possible to generate
a large collection of poses through isometric transformations of
the surface.37 Nevertheless, this still does not cover the exhaustive
bestiary of shapes that could be derived from a single animated
object. Very realistic animations routinely include for instance a
number of non-isometric deformations in order to model muscles
and the associated skin stretching movements.
Limitations of Existing 3D Watermarking Schemes
When facing large variability, a routine watermarking practice
consists in defining an embedding domain that will absorb most of
the instability so that the baseline watermark channel only has to deal
with marginal noise. Such embedding domain typically relies on some
signal processing transforms, which exhibit appealing properties
to achieve robustness. Should it be for audio, images or video, the
transforms considered in watermarking have evolved over the years
in a very similar pattern. Early algorithms essentially considered raw
data, also referred to as the time or the spatial domain; subsequent
works then relied on a frequency transform; finally, most recent
proposals rely on some space-frequency representation of the host
signal. 3D watermarking is no exception and also closely followed this
trend, as can be inferred by reading recent comprehensive surveys.38
Still, 3D specific adjustments had to be incorporated here and there
in order to obtain a dedicated content adaptation layer.
Pioneer 3D watermark algorithms essentially scanned through the
faces of the object to be protected and sequentially modified a
particular feature e.g. the ratio of the edges, the area, the projection
of one vertex onto the opposite edge, etc. These approaches
have however been rapidly found to be extremely fragile, some
of them being defeated in some cases with a simple reordering of
the vertices. Significant effort has therefore been spent to devise
alternate embedding domains that were directly derived from the
raw representation of the 3D object and that offered a superior
stability against attacks. For instance, a very popular scheme, which
has led to numerous variations afterwards, consists in modifying the
histogram of the distances between the vertices and the center of
mass of the object.39 While such techniques are often straightforward
to implement and fast to execute, they usually lack robustness in
many aspects e.g. with respect to cropping, smoothing, re-meshing,
etc.
37 An isometry is a transformation that preserves the distance between
points. For a 3D surface, it implies that the geodesic distance over the
surface remains untouched. For instance, the distance from the tip of a
finger to the shoulder is the same regardless of whether the arm is extended
or contracted.
38 Kai Wang et al., ‘A Comprehensive Survey on Three-Dimensional
Mesh Watermarking’, IEEE Transactions on Multimedia 10, no. 8 (December
2008): 1513–1527, doi:10.1109/TMM.2008.2007350.
39 J. -W Cho, R. Prost, and H. -Y Jung, ‘An Oblivious Watermarking for
3-D Polygonal Meshes Using Distribution of Vertex Norms’, IEEE Transactions on Signal Processing 55, no. 1 (January 2007): 142–155, doi:10.1109/
TSP.2006.882111.
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These shortcomings of the raw domain naturally led to investigating
frequency representations of the 3D mesh.40 The motivation
for considering such representations is threefold: (i) the spectral
domain can naturally be robust to geometric attacks (smoothing,
similarity transformations, etc.), (ii) the modifications introduced
by the watermarking process are mechanically spread throughout
the mesh at synthesis time (when performing the inverse transform
from the spectral domain to the raw domain), and (iii) the spectral
domain may offer a better control over the perceptual distortion
introduced by the watermarking process.41 Still, the 3D spectral
representation has a notable difference compared to its audiovisual
counterparts. There are no canonical reference basis vectors available
to decompose input 3D objects in the spectral domain and they
have therefore to be defined in a content dependent manner by
computing the eigenvectors of some intermediary representation
of the object, namely the Laplacian matrix.42 The issue with such
content-dependent spectral domain is that it makes the resulting
watermarking algorithm inherently weak against topological attacks
e.g. re-sampling attacks.
Due to the implementation constraints induced by the spectral
domain computation, researchers suggested using a multi-resolution
domain that is comparable to some extent to the wavelet transform
for audiovisual content. The multi-resolution representation of a
3D mesh indeed consists of a collection of versions of the mesh at
decreasing levels of details, together with wavelet coefficients that
enable going from one version of the mesh to another one of nearby
level of detail. A state-of-the-art watermarking algorithm can then
be applied to the coarsest wavelet coefficients for instance.43 While,
this alternate strategy is significantly less computationally demanding
than the spectral domain, it still shows high sensitivity to topological
attacks.
In summary, the vast majority of the published literature on 3D
watermarking solely focuses on rigid meshes. In this context,
improved robustness against geometric attacks has been reported
but none of the embedding domains proposed so far offers verified
robustness against topology attacks such as re-meshing. In addition,
since these watermarking schemes were not considering animation
in the first place, most of them will not be able to retrieve the
40 Ohbuchi R., Mukaiyama A., and Takahashi S., ‘A Frequency-Domain
Approach to Watermarking 3D Shapes’, Computer Graphics Forum 21, no. 3
(2002): 373–382, doi:10.1111/1467-8659.t01-1-00597.
41 In contrast with audiovisual content, one peculiar characteristic of 3D
content is that watermarks inserted in low frequencies are reported to be less
perceptible than the one inserted in high frequencies.
42 For a 3D object made of N vertices, the Laplacian matrix has dimension N×N and rapidly raises computational complexity concerns for large 3D
objects due to memory space constraints. A typical work-around consists in
partitioning the mesh in elements for which the Laplacian matrix could be
computed. It should be noted that various definition of the Laplacian matrix
exist, each one leading to a spectral domain having different properties.
There is currently no firm evidence hinting that a particular definition would
be more appropriate for watermarking purpose.
43 Kai Wang et al., ‘Hierarchical Blind Watermarking of 3D Triangular
Meshes’, in 2007 IEEE International Conference on Multimedia and Expo
(presented at the 2007 IEEE International Conference on Multimedia and
Expo, IEEE, 2007), 1235–1238, doi:10.1109/ICME.2007.4284880.
embedded watermark after the generation of a new pose. Only
a handful of articles explicitly addressed the issue of animation
e.g. by watermarking the individual temporal trajectories of the
vertices.44 Although such an approach manages to protect a single
animation generated from an animated 3D object, it fails to provide
a watermark that could be reliably extracted from whichever arbitrary
pose that could be generated with a rigged 3D mesh.
Concluding Remarks
Protecting computer generated 3D graphics received marginal
attention up to now. Both encryption and watermarking techniques
may need to be revisited in order to better accommodate for the
specificities of 3D content. On the encryption side, the adaptation of
format preserving encryption techniques offers intriguing prospects
for protecting 3D content while geometrically controlling the visual
distortion at rendering. However, this approach raises a number
of challenges which will need to be addressed, including security
evaluation due to information potentially leaking from unprotected
3D components and interfering interplay with other mechanisms
for 3D content (compression, steaming, rendering, etc). On the
watermarking front, the current lack of robustness of the proposed
solutions against re-meshing and pose generation calls for a drastic
paradigm shift. In this perspective, 3D mesh intrinsic properties —
such as geodesic distances, distance to the medial axis, etc. — appear
to be promising candidates for watermark embedding since they are
expected to offer natural robustness against topology attacks. A first
step in this direction has recently been taken.45
X. ROLLAND-NEVIÈRE,
Y. MAETZ,
M. ÉLUARD,
and G. DOËRR.
44 S. Yamazaki, ‘Watermarking motion data’, in Proc. Pacific Rim Workshop on Digital Steganography (STEG04), 2004, 177–185.
45 Jen-Sheng Tsai et al., ‘Geodesic distance-based pose-invariant blind
watermarking algorithm for three-dimensional triangular mesh model’, in
2010 17th IEEE International Conference on Image Processing (ICIP) (presented at the 2010 17th IEEE International Conference on Image Processing
(ICIP), IEEE, 2010), 209–212, doi:10.1109/ICIP.2010.5652120.
14
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1st International Workshop on Network Forensics, Security and Privacy (NFSP 2012),
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• Paper presentation: An empirical study of passive 802.11 device fingerprinting, by Christoph Neumann, Olivier Heen, and Stéphane Onno
10ième Symposium sur la Sécurité des Technologies de l’Information et des Communications (SSTIC 2012),
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• Chairman: Olivier Heen
5th IFIP International Conference on New Technologies, Mobility and Security (NTMS 2012),
Istanbul, Turkey, May 7-10, 2012
• Paper presentation: Improving the resistance to side-channel attacks on cloud storage services, by Olivier Heen, Christoph Neumann, Luis
Montalvo, and Serge Defrance
Information Security Practice and Experience (ISPEC 2012),
Hangzhou, China, April 9-12, 2012
• Paper presentation: Partial key exposure on RSA with private exponents larger than N, by Marc Joye, and Tancrède Lepoint
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