Improved quantization index modulation watermarking robust against amplitude scaling distortions

The original quantization index modulation (QIM) watermarking is largely vulnerable to volumetric scaling. To solve this problem, a gain-invariant vector for quantization is constructed through dividing the host signal by a statistical feature extracted from the target content. With this idea, the improved dither modulation (IM-DM) and spread transform dither modulation (IM-STDM) are developed in this paper. We present the strategies on the choice of the introduced feature and analyze the performance of two improved QIM schemes theoretically. It is shown that the improved QIM is theoretically invariant to valumetric scaling, but becomes sensitive to constant change. Experiments on real data (images) demonstrate that the proposed methods are extremely robust to amplitude scaling and possess the similar performance to the original QIM subject to several other typical attacks.