An entropy masking model for multimedia content watermarking

Inserting maximal allowable transparent watermark, which in turn is extremely hard to attack with common image processing operations, is important aspect of watermark design. We present a new watermark design tool for digital images and digital videos that are based on human visual system (HVS) characteristics. In this tool, basic mechanisms of inhibitory and excitatory behaviour of HVS cells are used to determine image dependent upper bound values on watermark insertion. This allows us to insert maximal allowable transparent watermark, which in turn is extremely hard to attack with common image processing, motion picture experts group (MPEG) compression. As the number of image details (e.g. edges) increases in an image, the HVS decreases its sensitivity to the image details. Similarly, the HVS decreases its sensitivity to the object motions as the number of motion increases in a video signal. We model this decreased sensitivity to the image details and object motions as an entropy masking. Entropy masking model can be efficiently used to increase the robustness of image and video watermarks. We have shown that our entropy-masking model provides watermark scheme with increased transparency and henceforth increased robustness.