Adaptive Blind Watermarking Using Psychovisual Image Features
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Abstract (arXiv)
With the growth of editing and sharing images through the internet, the importance of protecting the images' authorship has increased. Robust watermarking is a known approach to maintaining copyright protection. Robustness and imperceptibility are two factors that are tried to be maximized through watermarking. Usually, there is a trade-off between these two parameters. Increasing the robustness would lessen the imperceptibility of the watermarking. This paper proposes an adaptive method that determines the strength of the watermark embedding in different parts of the cover image regarding its texture and brightness. Adaptive embedding increases the robustness while preserving the quality of the watermarked image. Experimental results also show that the proposed method can effectively reconstruct the embedded payload in different kinds of common watermarking attacks. Our proposed method has shown good performance compared to a recent technique.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 25 Dec 2022.
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- 2021Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps
- 2022Go Wide or Go Deep: Levering Watermarking Performance with Computational Cost for Specific Images ↗
- 2025Pixel Seal: Adversarial-only training for invisible image and video watermarking
- 2023Leveraging Optimization for Adaptive Attacks on Image Watermarks
- 2022Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
- 2025Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation
- 2025Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection
- 2023A Somewhat Robust Image Watermark against Diffusion-based Editing Models