Sparse Watermark Embedding and Recovery Using Compressed Sensing Framework for Audio Signals

In this paper a new watermark embedding and recovery technique is proposed based on the compressed sensing framework. Both the watermark and the host signal are assumed to be sparse, each in its own domain. In recovery, the L1-minimization is used to recover the watermark and the host signal perfectly in clean conditions. The proposed technique is tested on MP3 audio where the effects of MP3 compression/decompression, sampling rate reduction and additive noise attacks are considered and bit error rate is compared with spread spectrum embedding. The proposed technique offers significantly better performance in all tested conditions and opens a new research approach for watermark embedding and recovery.