A robust watermarking algorithm against JPEG compression based on multiscale autoencoder

Abstract

The network structure of digital watermarking algorithm based on deep learning is usually encoder-noise layer-decoder. Most of the existing encoders suffer from the problem of insufficient feature extraction, and the introduction of simulated differentiable joint photographic experts group (JPEG) compression in the noise layer cannot ensure the robustness under real JPEG. In this paper, a watermarking algorithm based on multi-scale auto-encoder is proposed, which can effectively extract the image feature information by combining with the channel attention mechanism. At the same time, some parameters of decoder and encoder are shared to reduce redundant feature embedding and improve extraction accuracy. This paper also proposes a robust training scheme against JPEG compression, which can guide the model to store the watermark in the low-frequency region needed for decoding. Experimental results show that the peak signal-to-noise ratio (PSNR) of the proposed algorithm is above 48 and the decoding rate is above 99% under JPEG compression with quality factor Q = 50. Moreover, this scheme can effectively promote the combination of noise layer in training. In addition, the proposed algorithm is also robust to other common network noises.