488 research outputs found
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
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
Fingerprinting with Minimum Distance Decoding
This work adopts an information theoretic framework for the design of
collusion-resistant coding/decoding schemes for digital fingerprinting. More
specifically, the minimum distance decision rule is used to identify 1 out of t
pirates. Achievable rates, under this detection rule, are characterized in two
distinct scenarios. First, we consider the averaging attack where a random
coding argument is used to show that the rate 1/2 is achievable with t=2
pirates. Our study is then extended to the general case of arbitrary
highlighting the underlying complexity-performance tradeoff. Overall, these
results establish the significant performance gains offered by minimum distance
decoding as compared to other approaches based on orthogonal codes and
correlation detectors. In the second scenario, we characterize the achievable
rates, with minimum distance decoding, under any collusion attack that
satisfies the marking assumption. For t=2 pirates, we show that the rate
is achievable using an ensemble of random linear
codes. For , the existence of a non-resolvable collusion attack, with
minimum distance decoding, for any non-zero rate is established. Inspired by
our theoretical analysis, we then construct coding/decoding schemes for
fingerprinting based on the celebrated Belief-Propagation framework. Using an
explicit repeat-accumulate code, we obtain a vanishingly small probability of
misidentification at rate 1/3 under averaging attack with t=2. For collusion
attacks which satisfy the marking assumption, we use a more sophisticated
accumulate repeat accumulate code to obtain a vanishingly small
misidentification probability at rate 1/9 with t=2. These results represent a
marked improvement over the best available designs in the literature.Comment: 26 pages, 6 figures, submitted to IEEE Transactions on Information
Forensics and Securit
On Anti-Collusion Codes and Detection Algorithms for Multimedia Fingerprinting
Multimedia fingerprinting is an effective technique to trace the sources of pirate copies of copyrighted multimedia information. AND anti-collusion codes can be used to construct fingerprints resistant to collusion attacks on multimedia contents. In this paper, we first investigate AND anti-collusion codes and related detection algorithms from a combinatorial viewpoint, and then introduce a new concept of logical anti-collusion code to improve the traceability of multimedia fingerprinting. It reveals that frameproof codes have traceability for multimedia contents. Relationships among anti-collusion codes and other structures related to fingerprinting are discussed, and constructions for both AND anti-collusion codes and logical anti-collusion codes are provided
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