A bilateral attention based generative adversarial network for DIBR 3D image watermarking

Abstract

This paper presents a bilateral attention based generative adversarial network (BAGAN) for depth-image-based rendering (DIBR) 3D image watermarking to protect the image copyright. Convolutional block operations are employed to extract main image features for robust watermarking, but embedding watermark into some features will degrade image quality much. To relieve this kind of image distortion, the bilateral attention module (BAM) is utilized by mining correlations of the center view and the depth map to compute attention of the 3D image for guiding watermark to distribute over different image regions. Since a modality gap exists between the center view and the depth map, a cross-modal feature fusion module (CMFFM) is designed for BAM to bridge the cross-view gap. Because the depth map has lots of flat background information including many redundant features, to prune them, the depth redundancy elimination module (DREM) is used for cross-view feature fusion. In the decoder, two extractors with the same structure are built to recover watermark from the center view and the synthesized view, respectively. In addition, the discriminator is supposed to build a competitive relationship with the encoder to increase the image quality. The noise sub-network is used to train different image attacks for robustness. Extensive experimental results have demonstrated that the proposed BAGAN can obtain higher watermarking invisibility and robustness compared with existing DIBR 3D watermarking methods. Ablation experiments have also proven the effectiveness of DREM, CMFFM and BAM on BAGAN.