CADW: CGAN-Based Attack on Deep Robust Image Watermarking

Robust watermarking plays an essential role in copyright protection for digital images. Meanwhile, the studies of robust image watermarking and corresponding attack methods promote and complement each other. Because the traditional attack methods are weak to attack the deep watermarking, in this work, we thus design an effective watermarking attack method based on the conditional generative adversarial nets (CGAN). Also, according to the network structure of CGAN, a targeted and combined loss function is constructed, which can guarantee an acceptable visual quality of attacked image and make the watermark extraction fail simultaneously. Experimental results demonstrate that our method is effective for some state-of-the-art deep robust image watermarking methods with the transferability. In addition, as an attack method, it can be regarded as an evaluation standard to measure the robustness of watermarking.