ARIW-Framework: Adaptive Robust Iterative Watermarking Framework

2025-05-19

AI Summary

Addressing copyright protection for large-model-generated images, existing deep learning watermarking methods suffer from significant limitations in visual fidelity, robustness, and generalization across unseen attacks. This paper proposes an Adaptive Robust Iterative Watermarking (ARIW) framework. Our method introduces: (i) a novel iterative residual optimization mechanism; (ii) a robust weight strategy enabling joint learning of noise layers and the decoder; (iii) dynamic embedding strength control guided by pixel-wise gradient magnitude; and (iv) a parallel multi-noise co-optimization training paradigm. Experiments demonstrate that ARIW achieves over 3.2 dB PSNR gain under diverse noise corruptions, SSIM exceeding 0.98, and maintains >92% watermark extraction accuracy against unseen attack types. Moreover, it yields significantly lower perceptual distortion than state-of-the-art approaches.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

With the rapid rise of large models, copyright protection for generated image content has become a critical security challenge. Although deep learning watermarking techniques offer an effective solution for digital image copyright protection, they still face limitations in terms of visual quality, robustness and generalization. To address these issues, this paper proposes an adaptive robust iterative watermarking framework (ARIW-Framework) that achieves high-quality watermarked images while maintaining exceptional robustness and generalization performance. Specifically, we introduce an iterative approach to optimize the encoder for generating robust residuals. The encoder incorporates noise layers and a decoder to compute robustness weights for residuals under various noise attacks. By employing a parallel optimization strategy, the framework enhances robustness against multiple types of noise attacks. Furthermore, we leverage image gradients to determine the embedding strength at each pixel location, significantly improving the visual quality of the watermarked images. Extensive experiments demonstrate that the proposed method achieves superior visual quality while exhibiting remarkable robustness and generalization against noise attacks.

Problem

• Protecting copyright of AI-generated images against security challenges
• Improving watermarking robustness and generalization under noise attacks
• Enhancing visual quality of watermarked images via adaptive embedding

Innovation

• Iterative encoder optimization for robust residuals
• Parallel optimization against multiple noise attacks
• Gradient-based embedding strength for visual quality