Watermark Detection after Quantization Attacks

The embedding of additive noise sequences is often used to hide information in digital audio, image or video documents. However, the embedded information might be impaired by involuntary or malicious “attacks.” This paper shows that quantization attacks cannot be described appropriately by an additive white Gaussian noise (AWGN) channel. The robustness of additive watermarks against quantization depends strongly on the distribution of the host signal. Common compression schemes decompose a signal into sub-signals (e.g., frequency coefficients) and then adapt the quantization to the characteristics of the subsignals. This has to be considered during watermark detection. A maximum likelihood (ML) detector that can be adapted to watermark sub-signals with different robustness is developed. The performance of this detector is investigated for the case of image watermark detection after JPEG compression.