High-Capacity Robust Watermarking Technology for High-Resolution Images
Computer Vision and Pattern Recognition
Summary
The authors developed a new way to hide large watermarks (up to 4 KB) inside very detailed images (1024x1024 pixels) without hurting the image quality much. They use a technique that breaks the image into blocks and carefully embeds the watermark in each part, helping the system learn efficiently even with limited resources. Their design uses special networks to embed and recover the watermark in a reversible manner, keeping the watermark and image balanced. They also optimize their method to keep the image looking good while making the watermark strong against noise and attacks. Tests show their method works well for large, detailed images and can resist many types of interference.
Authors
Shaowu Wu, Wei Lu, Qing Qian, Mingsen Deng
Abstract
Most existing watermarking techniques are primarily designed for low-resolution images, with few methods tailored for high-resolution images. Moreover, the embedding capacity is often limited to fixed lengths (e.g., 30, 100, 256 bits, etc.), which struggles to meet practical demands. To address these issues, this paper proposes a high-capacity robust watermarking method for high-resolution images, capable of embedding a watermark of 4 KB (32,768 bits) into images with a resolution of 1024*1024, achieving an embedding rate of 0.0313 bpp. Specifically, this paper adopts a block-wise strategy to effectively embed the watermark into local regions, enabling the network to train and learn normally even under low-resource conditions. The encoder and decoder structures respectively employ a reversible symmetric architecture with three convolutional and three deconvolutional layers, ensuring consistency in the coupling and decoupling of the watermark and image features. Additionally, the loss function combines global and local losses with weighted contributions. By incorporating constraints on the visual quality and robustness of local block regions, the overall imperceptibility and robustness of the image are further enhanced. Extensive experimental results verify that the proposed method is effective and feasible in high-resolution image scenarios with high-capacity watermarking, while demonstrating strong robustness against various noise attacks.