Concealed Attack for Robust Watermarking Based on Generative Model and Perceptual Loss

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 8; pp. 5695 - 5706
• Main Authors: Li, Qi, Wang, Xingyuan, Ma, Bin, Wang, Xiaoyu, Wang, Chunpeng, Gao, Suo, Shi, Yunqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coders; concealed attack; Encoders-Decoders; Feature extraction; generative adversarial network; Generative adversarial networks; Generators; Image quality; imperceptibility; Neural networks; Optimization; perceptual losses; Robustness; Transforms; Watermarking

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. F...