401 research outputs found
Fast watermarking of MPEG-1/2 streams using compressed-domain perceptual embedding and a generalized correlator detector
A novel technique is proposed for watermarking of MPEG-1 and MPEG-2 compressed video streams. The proposed scheme is applied directly in the domain of MPEG-1 system streams and MPEG-2 program streams (multiplexed streams). Perceptual models are used during the embedding process in order to avoid degradation of the video quality. The watermark is detected without the use of the original video sequence. A modified correlation-based detector is introduced that applies nonlinear preprocessing before correlation. Experimental evaluation demonstrates that the proposed scheme is able to withstand several common attacks. The resulting watermarking system is very fast and therefore suitable for copyright protection of compressed video
Capacities and Capacity-Achieving Decoders for Various Fingerprinting Games
Combining an information-theoretic approach to fingerprinting with a more
constructive, statistical approach, we derive new results on the fingerprinting
capacities for various informed settings, as well as new log-likelihood
decoders with provable code lengths that asymptotically match these capacities.
The simple decoder built against the interleaving attack is further shown to
achieve the simple capacity for unknown attacks, and is argued to be an
improved version of the recently proposed decoder of Oosterwijk et al. With
this new universal decoder, cut-offs on the bias distribution function can
finally be dismissed.
Besides the application of these results to fingerprinting, a direct
consequence of our results to group testing is that (i) a simple decoder
asymptotically requires a factor 1.44 more tests to find defectives than a
joint decoder, and (ii) the simple decoder presented in this paper provably
achieves this bound.Comment: 13 pages, 2 figure
Asymptotics of Fingerprinting and Group Testing: Capacity-Achieving Log-Likelihood Decoders
We study the large-coalition asymptotics of fingerprinting and group testing,
and derive explicit decoders that provably achieve capacity for many of the
considered models. We do this both for simple decoders (fast but suboptimal)
and for joint decoders (slow but optimal), and both for informed and uninformed
settings.
For fingerprinting, we show that if the pirate strategy is known, the
Neyman-Pearson-based log-likelihood decoders provably achieve capacity,
regardless of the strategy. The decoder built against the interleaving attack
is further shown to be a universal decoder, able to deal with arbitrary attacks
and achieving the uninformed capacity. This universal decoder is shown to be
closely related to the Lagrange-optimized decoder of Oosterwijk et al. and the
empirical mutual information decoder of Moulin. Joint decoders are also
proposed, and we conjecture that these also achieve the corresponding joint
capacities.
For group testing, the simple decoder for the classical model is shown to be
more efficient than the one of Chan et al. and it provably achieves the simple
group testing capacity. For generalizations of this model such as noisy group
testing, the resulting simple decoders also achieve the corresponding simple
capacities.Comment: 14 pages, 2 figure
ICA for watermarking digital images
A domain independent ICA-based approach to watermarking is presented. This approach can be used on images, music or video to embed either a robust or fragile watermark. In the case of robust watermarking, the method shows high information rate and robustness against malicious and non-malicious attacks, while keeping a low induced distortion. The fragile watermarking scheme, on the other hand, shows high sensitivity to tampering attempts while keeping the requirement for high information rate and low distortion. The improved performance is achieved by employing a set of statistically independent sources (the independent components) as the feature space and principled statistical decoding methods. The performance of the suggested method is compared to other state of the art approaches. The paper focuses on applying the method to digitized images although the same approach can be used for other media, such as music or video
Adaptive White-Box Watermarking with Self-Mutual Check Parameters in Deep Neural Networks
Artificial Intelligence (AI) has found wide application, but also poses risks
due to unintentional or malicious tampering during deployment. Regular checks
are therefore necessary to detect and prevent such risks. Fragile watermarking
is a technique used to identify tampering in AI models. However, previous
methods have faced challenges including risks of omission, additional
information transmission, and inability to locate tampering precisely. In this
paper, we propose a method for detecting tampered parameters and bits, which
can be used to detect, locate, and restore parameters that have been tampered
with. We also propose an adaptive embedding method that maximizes information
capacity while maintaining model accuracy. Our approach was tested on multiple
neural networks subjected to attacks that modified weight parameters, and our
results demonstrate that our method achieved great recovery performance when
the modification rate was below 20%. Furthermore, for models where watermarking
significantly affected accuracy, we utilized an adaptive bit technique to
recover more than 15% of the accuracy loss of the model
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