Digital Watermark Embedding and Detection Using DCT and HVS

Digital watermark embedding and detection using DCT (Discrete Cosine Transform), developed by author Mao Li. The algorithm's purpose is to embed watermarks into images through DCT blocking and Human Visual System (HVS) adaptation, followed by watermark detection using similarity comparison metrics. In digital watermark embedding and detection technology, DCT serves as a fundamental method that divides images into non-overlapping blocks and performs frequency domain transformation on each block. The implementation typically involves selecting mid-frequency DCT coefficients for watermark embedding since they provide good balance between robustness and visual quality. The algorithm modifies these coefficients according to watermark bits while maintaining perceptual transparency. To enhance watermark detection performance, the algorithm incorporates HVS characteristics that make human vision more sensitive to certain image details and changes. Code implementation often includes contrast sensitivity function (CSF) modeling and luminance adaptation to determine optimal embedding strength. This HVS-DCT combination enables more precise watermark detection by adjusting embedding parameters based on visual perception thresholds. The algorithm's author Mao Li has contributed significantly to digital watermarking research and development. His method not only implements effective watermark embedding through systematic DCT coefficient manipulation but also provides reliable detection mechanisms using correlation-based detection functions. The core detection algorithm typically computes similarity measures between extracted watermark patterns and reference watermarks, often employing correlation coefficients or normalized cross-correlation for robust performance. In summary, the DCT blocking and HVS-adapted algorithm provides a comprehensive solution for digital watermark embedding and detection. The technical implementation involves block-based DCT processing, adaptive embedding strength calculation using HVS models, and statistical detection methods, offering valuable support for digital copyright protection and information security applications.