Self-Recovery Robust Image Watermarking

The reversibility of robust reversible watermarking (RRW) strictly depends on the premise that the watermarked image has not been attacked. However, in practical applications, multimedia content often suffers from various attacks, and existing RRW technologies completely lose their reversible recovery capabilities. Therefore, this paper proposes a self-recovery robust watermarking (SRRW) algorithm, which provides a new ability to recover the watermarked and attacked image more closely to the original image while the watermark maintains strong robustness to those common signal processing operations and geometric attacks. First, the watermark information is inserted into low-order Zernike moments of a cover image by using quantization watermarking technique so that the watermark is resistant to those additive noise- like operations ( like JPEG compression and Gaussian noises) and invariant to geometric transforms ( like rotation and scaling). Then, the scaled difference vectors between the cover vectors and its quantized watermarked vectors are luminously and ingeniously computed as the distortion compensation information added back to the quantized watermarked vectors for restoration of the cover image. Finally, at the receiver side, based on the watermark bits extracted from the watermarked image or its distorted versions, those Zernike moments embedded data can be restored by the known scaling factor of the difference vector. Furthermore, a self-recovery image resembling the original image more closely can be reconstructed. Experimental results show that the proposed SRRW scheme can effectively restore those distorted images due to the 128-bit watermark embedding, JPEG compression with the quality factor 60 or JPEG2000 compression with the compression ratio 9, while providing strong robustness performance to various kinds of attacks. Compared with the attacked watermarked images, the restored images show PSNR improvements of 1.66 dB, 3.53 dB, and 1.72 dB under JPEG compression ($Q = 90$), JPEG2000 compression ($R = 3$), and AWGN ($\sigma = 0.0001$), respectively.