8,181 research outputs found
Gossip Codes for Fingerprinting: Construction, Erasure Analysis and Pirate Tracing
This work presents two new construction techniques for q-ary Gossip codes
from tdesigns and Traceability schemes. These Gossip codes achieve the shortest
code length specified in terms of code parameters and can withstand erasures in
digital fingerprinting applications. This work presents the construction of
embedded Gossip codes for extending an existing Gossip code into a bigger code.
It discusses the construction of concatenated codes and realisation of erasure
model through concatenated codes.Comment: 28 page
On the Saddle-point Solution and the Large-Coalition Asymptotics of Fingerprinting Games
We study a fingerprinting game in which the number of colluders and the
collusion channel are unknown. The encoder embeds fingerprints into a host
sequence and provides the decoder with the capability to trace back pirated
copies to the colluders.
Fingerprinting capacity has recently been derived as the limit value of a
sequence of maximin games with mutual information as their payoff functions.
However, these games generally do not admit saddle-point solutions and are very
hard to solve numerically. Here under the so-called Boneh-Shaw marking
assumption, we reformulate the capacity as the value of a single two-person
zero-sum game, and show that it is achieved by a saddle-point solution.
If the maximal coalition size is k and the fingerprinting alphabet is binary,
we show that capacity decays quadratically with k. Furthermore, we prove
rigorously that the asymptotic capacity is 1/(k^2 2ln2) and we confirm our
earlier conjecture that Tardos' choice of the arcsine distribution
asymptotically maximizes the mutual information payoff function while the
interleaving attack minimizes it. Along with the asymptotic behavior, numerical
solutions to the game for small k are also presented.Comment: submitted to IEEE Trans. on Information Forensics and Securit
Discrete Distributions in the Tardos Scheme, Revisited
The Tardos scheme is a well-known traitor tracing scheme to protect
copyrighted content against collusion attacks. The original scheme contained
some suboptimal design choices, such as the score function and the distribution
function used for generating the biases. Skoric et al. previously showed that a
symbol-symmetric score function leads to shorter codes, while Nuida et al.
obtained the optimal distribution functions for arbitrary coalition sizes.
Later, Nuida et al. showed that combining these results leads to even shorter
codes when the coalition size is small. We extend their analysis to the case of
large coalitions and prove that these optimal distributions converge to the
arcsine distribution, thus showing that the arcsine distribution is
asymptotically optimal in the symmetric Tardos scheme. We also present a new,
practical alternative to the discrete distributions of Nuida et al. and give a
comparison of the estimated lengths of the fingerprinting codes for each of
these distributions.Comment: 5 pages, 2 figure
DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
This paper proposes DeepMarks, a novel end-to-end framework for systematic
fingerprinting in the context of Deep Learning (DL). Remarkable progress has
been made in the area of deep learning. Sharing the trained DL models has
become a trend that is ubiquitous in various fields ranging from biomedical
diagnosis to stock prediction. As the availability and popularity of
pre-trained models are increasing, it is critical to protect the Intellectual
Property (IP) of the model owner. DeepMarks introduces the first fingerprinting
methodology that enables the model owner to embed unique fingerprints within
the parameters (weights) of her model and later identify undesired usages of
her distributed models. The proposed framework embeds the fingerprints in the
Probability Density Function (pdf) of trainable weights by leveraging the extra
capacity available in contemporary DL models. DeepMarks is robust against
fingerprints collusion as well as network transformation attacks, including
model compression and model fine-tuning. Extensive proof-of-concept evaluations
on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural
networks architectures such as Wide Residual Networks (WRNs) and Convolutional
Neural Networks (CNNs), corroborate the effectiveness and robustness of
DeepMarks framework
Tardos fingerprinting is better than we thought
We review the fingerprinting scheme by Tardos and show that it has a much
better performance than suggested by the proofs in Tardos' original paper. In
particular, the length of the codewords can be significantly reduced.
First we generalize the proofs of the false positive and false negative error
probabilities with the following modifications: (1) we replace Tardos'
hard-coded numbers by variables and (2) we allow for independently chosen false
positive and false negative error rates. It turns out that all the
collusion-resistance properties can still be proven when the code length is
reduced by a factor of more than 2.
Second, we study the statistical properties of the fingerprinting scheme, in
particular the average and variance of the accusations. We identify which
colluder strategy forces the content owner to employ the longest code. Using a
gaussian approximation for the probability density functions of the
accusations, we show that the required false negative and false positive error
rate can be achieved with codes that are a factor 2 shorter than required for
rigid proofs.
Combining the results of these two approaches, we show that the Tardos scheme
can be used with a code length approximately 5 times shorter than in the
original construction.Comment: Modified presentation of result
Towards joint decoding of binary Tardos fingerprinting codes
The class of joint decoder of probabilistic fingerprinting codes is of utmost
importance in theoretical papers to establish the concept of fingerprint
capacity. However, no implementation supporting a large user base is known to
date. This article presents an iterative decoder which is, as far as we are
aware of, the first practical attempt towards joint decoding. The
discriminative feature of the scores benefits on one hand from the
side-information of previously accused users, and on the other hand, from
recently introduced universal linear decoders for compound channels. Neither
the code construction nor the decoder make precise assumptions about the
collusion (size or strategy). The extension to incorporate soft outputs from
the watermarking layer is straightforward. An extensive experimental work
benchmarks the very good performance and offers a clear comparison with
previous state-of-the-art decoders.Comment: submitted to IEEE Trans. on Information Forensics and Security. -
typos corrected, one new plot, references added about ECC based
fingerprinting code
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