An image watermark parameter optimization procedure is proposed for selecting the most effective DCT coefficients for watermark embedding. Using this set of coefficients improves the watermark robustness and reliability against attack while it maintains the transparency of the embedded watermark. With the aid of prior knowledge of attacks, the visual masking effect and the attack distortion on each (DCT) transform coefficient are pre-calculated so that a maximum strength watermark within visual threshold can be inserted. There are two stages in the design phase. First, taking into account the combined effect of watermark embedding and attack, we pick up the robust coefficients that resist a specific type of attacks and in the meanwhile we keep the distortion lower than the visual threshold. Although typically the watermark detection reliability increases with the increasing number of embedded coefficients, the less effective coefficients may degrade the overall detection performance. Thus, in the second stage, some initially selected coefficients are discarded by an iterative process to reduce the overall error detection probability. Since digital images are often compressed for efficient storage and transmission, we adopt JPEG compression as the attacking source. The simulation results show that the detection error probability is significantly reduced when the selected robust coefficients are in use. These coefficients with watermark embedded on them can also survive color reduction, Gaussian filtering, and frequency mode Laplacian removal (FMLR) attacks.
Proc. SPIE. 4675, Security and Watermarking of Multimedia Contents IV
KEYWORDS: Optical filters, Digital filtering, Linear filtering, Digital watermarking, Feature extraction, Gaussian filters, Digital imaging, Signal processing, Multimedia, Filtering (signal processing)
A novel robust digital image watermarking scheme which combines image feature extraction and image normalization is proposed. The goal is to resist both geometrical and signal processing attacks. We adopt a feature extraction method called Mexican Hat wavelet scale interaction. The extracted feature points can survive various attacks such as common signal processing, JPEG compression, and geometric distortions. Thus, these feature points can be used as reference points for both watermark embedding and detection. The normalized image of a rotated image (object) is the same as the normalized version of the original image. As a result, the watermark detection task can be much simplified when it is done on the normalized image without referencing to the original image. However, because image normalization is sensitive to image local variation, we apply image normalization to non-overlapped image disks separately. The center of each disk is an extracted feature point. Several copies of one 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, printing and scanning process, row or column removal, shearing, rotation, scaling, local warping, cropping, and linear transformation.