CL-DRW: Curriculum Learning-Based Deep Robust Watermarking for Social Networks

The development of the internet has greatly facilitated the transmission of images over social networks, while also triggering serious copyright issues. Deep robust watermarking serves as a crucial technique for image copyright protection. However, the image distortions caused by Social Network Transmission Operations (SNTOs) make existing deep robust watermarking methods fragile in real-world social network scenarios. To address this, we propose a Curriculum Learning-based Deep Robust Watermarking method, called CL-DRW, to generate watermarks that can be resilient to SNTOs. Specifically, we develop a watermarking model constructed with an invertible neural network and present a multi-stage training framework based on curriculum learning to train it effectively. We incrementally introduce noise attacks based on their disruptive impact on the watermark, from weak to strong, thereby enabling our model to build robustness against SNTOs gradually. Additionally, we design an SNTOs simulation noise layer, which is built upon a transformer-based deep network and incorporates differentiable JPEG, to simulate the black-box distortions caused by SNTOs. Extensive experiments indicate that our proposed CL-DRW outperforms state-of-the-art deep watermarking methods in terms of robustness against real-world social network transmission operations.