Abstract:

With the development of network and multimedia technology, multimedia data has gradually become a key source of information for people, making digital media the primary battlefield for copyright protection and anti-counterfeit traceability.Digital watermarking techniques have been widely studied and recognized as important tools for copyright protection.However, the robustness of conventional digital watermarking methods is limited as sensitive digital media can easily be affected by noise and external interference during transmission.Then the existing powerful digital watermarking technology’s comprehensive resistance to all forms of attacks must be enhanced.Moreover, the conventional robust digital watermarking algorithm’s generalizability across a variety of image types is limited due to its embedding method.Deep learning has been widely used in the development of robust digital watermarking systems due to its self-learning abilities.However, current initialization techniques based on deep neural networks rely on random parameters and features, resulting in low-quality model generation, lengthy training times, and potential convergence issues.To address these challenges, a deep robust digital watermarking algorithm based on a traditional bootstrapping mechanism was proposed.It combined the benefits of both traditional digital watermarking techniques and deep neural networks, taking into account their learning abilities and robust characteristics.The algorithm used the classic robust digital watermarking algorithm to make watermarked photos, and the constructed feature guaranteed the resilience of traditional watermarked images.The final dense image was produced by fusing the conventionally watermarked image with the deep network using the U-Net structure.The testing results demonstrate that the technique can increase the stego image’s resistance to various attacks and provide superior visual quality compared to the conventional algorithm.

Key words: watermarking technology, deep network, robust watermarking, U-Net