Robustness Enhancement of Content-Based Watermarks Using Entropy Masking Effect

Image-Adaptive watermarking systems exploit visual models to adapt the watermark to local properties of the host image. This leads to a watermark power enhancement, hence an improved resilience against different attacks, while keeping the mark imperceptible. Visual models consider different properties of the human visual system, such as frequency sensitivity, luminance sensitivity and contrast masking. Entropy masking is another human visual system’s characteristic, which rarely has been addressed in visual models. In this paper we have utilized this masking effect to improve the robustness of Image-Adaptive watermarks while keeping their transparency. Experimental results show a significant amount of enhancement to the power of watermark. The work has been expanded to video watermarking, considering special properties of the entropy masking effect.