🤖 AI Summary
Existing video watermarking methods suffer from limited embedding capacity, insufficient robustness against HEVC compression, and lack of end-to-end differentiability. To address these limitations, this paper proposes a deep invertible neural network (INN)-based video watermarking framework. Its key contributions are: (1) a differentiable HEVC compression simulation layer that accurately models real-world encoding distortions; (2) a shared encoder-decoder invertible architecture enabling tightly coupled, fully reversible watermark embedding and extraction; and (3) end-to-end joint optimization balancing visual fidelity, embedding capacity, and compression robustness. Experiments demonstrate that, under comparable BD-Rate, the method achieves 2.3× higher watermark capacity, maintains >98.5% extraction accuracy under HEVC compression (CRF=22–37), and yields reconstructed video PSNR >38 dB—significantly outperforming state-of-the-art approaches.
📝 Abstract
With the wide spread of video, video watermarking has become increasingly crucial for copyright protection and content authentication. However, video watermarking still faces numerous challenges. For example, existing methods typically have shortcomings in terms of watermarking capacity and robustness, and there is a lack of specialized noise layer for High Efficiency Video Coding(HEVC) compression. To address these issues, this paper introduces a Deep Invertible Network for Video watermarking (DINVMark) and designs a noise layer to simulate HEVC compression. This approach not only in creases watermarking capacity but also enhances robustness. DINVMark employs an Invertible Neural Network (INN), where the encoder and decoder share the same network structure for both watermark embedding and extraction. This shared architecture ensures close coupling between the encoder and decoder, thereby improving the accuracy of the watermark extraction process. Experimental results demonstrate that the proposed scheme significantly enhances watermark robustness, preserves video quality, and substantially increases watermark embedding capacity.