Robust watermarking of compressive sensed measurements under impulsive and Gaussian attacks

Abstract

This paper considers the watermark embedding problem onto Compressive Sensed measurements of a signal that is sparse in a proper basis. We propose a novel watermark encoding-decoding algorithm that exploits the sparsity of the signal to achieve dense watermarking. The proposed algorithm is robust under additive white Gaussian noise as well as impulsive noise or their mixture. The experimental results show also that the algorithm achieves an embedding capacity superior to those of classical ℓ2 and ℓ1 embedding algorithms.