A Novel Gain Invariant Quantization-Based Watermarking Approach

This paper presents a novel quantization-based information hiding approach, which is invariant to gain attack. In the presented scheme, the host signal vector is first divided into two parts, then l_p-norm of each vector is calculated. The watermark bits are embedded by quantizing the ratio of the l_p-norm of each part. The decoding scheme is performed blindly using the Euclidean distance. Due to the use of division function, the proposed method is robust to scaling attacks. The analytical error probability and embedding distortion are derived and assessed by simulations on artificial signals. The optimum parameter in the l_p-norm function is obtained based on minimizing the error probability. Experimental results confirm the superiority of the proposed technique against common attacks in comparison with the existing state-of-the-art methods.