Asymptotically false-positive-maximizing attack on non-binary Tardos codes

We use a method recently introduced by Simone and Skoric to study accusation probabilities for non-binary Tardos fingerprinting codes. We generalize the pre-computation steps in this approach to include a broad class of collusion attack strategies. We analytically derive properties of a special attack that asymptotically maximizes false accusation probabilities. We present numerical results on sufficient code lengths for this attack, and explain the abrupt transitions that occur in these results

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

Anticollusion solutions for asymmetric fingerprinting protocols based on client side embedding

In this paper, we propose two different solutions for making a recently proposed asymmetric fingerprinting protocol based on client-side embedding robust to collusion attacks. The first solution is based on projecting a client-owned random fingerprint, securely obtained through existing cryptographic protocols, using for each client a different random matrix generated by the server. The second solution consists in assigning to each client a Tardos code, which can be done using existing asymmetric protocols, and modulating such codes using a specially designed random matrix. Suitable accusation strategies are proposed for both solutions, and their performance under the averaging attack followed by the addition of Gaussian noise is analytically derived. Experimental results show that the analytical model accurately predicts the performance of a realistic system. Moreover, the results also show that the solution based on independent random projections outperforms the solution based on Tardos codes, for different choices of parameters and under different attack models

Tardos fingerprinting codes in the combined digit model

We introduce a new attack model for collusion secure codes, and analyze the collusion resistance of two version of the Tardos code in this model, both for binary and non-binary alphabets. The model allows to consider signal processing and averaging attacks via a set of symbol detection error rates. The false positive rate is represented as a single number; the false negative rate is a function of the false positive rate and of the number of symbols mixed by the colluders. We study two versions of the q-ary Tardos code in which the accusation method has been modified so as to allow for the detection of multiple symbols in the same content segment. The collusion resilience of both variants turns out to be comparable. For realistic attacker strengths the increase in code length is modest, demonstrating that the modified Tardos code is effective in the new model

Tardos fingerprinting codes in the combined digit model

We introduce a new attack model for collusion secure codes, and analyze the collusion resistance of two version of the Tardos code in this model, both for binary and non-binary alphabets. The model allows to consider signal processing and averaging attacks via a set of symbol detection error rates. The false positive rate is represented as a single number; the false negative rate is a function of the false positive rate and of the number of symbols mixed by the colluders. We study two versions of the q-ary Tardos code in which the accusation method has been modified so as to allow for the detection of multiple symbols in the same content segment. The collusion resilience of both variants turns out to be comparable. For realistic attacker strengths the increase in code length is modest, demonstrating that the modified Tardos code is effective in the new model

Tardos fingerprinting codes in the combined digit model

We formalize a new attack model for collusion secure codes, incorporating attacks on the underlying watermarking scheme as well as cut-and-paste attacks traditionally considered for collusion secure codes. We use this model to analyze the collusion resistance of two versions of the Tardos code, both for binary and nonbinary alphabets. The model allows us to consider different signal processing attacks on the content, namely the addition of noise and averaging attacks. The latter may result in content segments that have multiple watermarks embedded. We study two versions of the q-ary Tardos code in which the accusation method has been modified so as to allow for the detection of multiple symbols in the same content segment. We show that both variants yield efficient codes in the new model, parametrized for realistic attacker strengths