ARIW-Framework: Adaptive Robust Iterative Watermarking Framework

TL;DR

The ARIW-Framework introduces an adaptive, iterative watermarking method that enhances visual quality, robustness, and generalization for protecting generated images against noise attacks.

Contribution

It presents a novel adaptive iterative watermarking framework that optimizes encoder robustness and visual quality using noise layers and gradient-based embedding.

Findings

Achieves high visual quality of watermarked images.

Demonstrates superior robustness against noise attacks.

Shows strong generalization across different noise types.

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.