Bayesian Watermark Attacks

Abstract: This paper presents an application of statistical machine learning to the field of watermarking. We propose a new attack model on additive spread-spectrum watermarking systems. The proposed attack is based on Bayesian statistics. We consider the scenario in which a watermark signal is repeatedly embedded in specific, possibly chosen based on a secret message bitstream, segments (signals) of the host data. The host signal can represent a patch of pixels from an image or a video frame. We propose a probabilistic model that infers the embedded message bitstream and watermark signal, directly from the watermarked data, without access to the decoder. We develop an efficient Markov chain Monte Carlo sampler for updating the model parameters from their conjugate full conditional posteriors. We also provide a variational Bayesian solution, which further increases the convergence speed of the algorithm. Experiments with synthetic and real image signals demonstrate that the attack model is able to correctly infer a large part of the message bitstream and obtain a very accurate estimate of the watermark signal.