ARIW-Framework: Adaptive Robust Iterative Watermarking Framework

• URL: http://arxiv.org/abs/2505.13101v1
• Date: Mon, 19 May 2025 13:31:48 GMT
• Title: ARIW-Framework: Adaptive Robust Iterative Watermarking Framework
• Authors: Shaowu Wu, Liting Zeng, Wei Lu, Xiangyang Luo,
• Abstract summary: This paper proposes an adaptive robust iterative watermarking framework (ARIW-Framework)<n>It achieves high-quality watermarked images while maintaining exceptional robustness and generalization performance.
• Score: 14.782580487951018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity [68.95168727940973]
  Tamper-Aware Generative image WaterMarking method named TAG-WM.<n>This paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM.
  arXiv  Detail & Related papers  (2025-06-30T03:14:07Z)
• Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models [50.73220224678009]
  Watermarking can be used to verify the origin of synthetic images generated by artificial intelligence models.<n>Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial techniques.<n>In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery.<n>Our approach significantly broadens the scope of attacks, presenting a greater challenge to the security of current watermarking techniques.
  arXiv  Detail & Related papers  (2025-06-06T12:08:02Z)
• Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.<n>We propose a diffusion model watermarking method tailored for real-world deployment.<n>Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
  arXiv  Detail & Related papers  (2025-04-21T11:18:16Z)
• Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking [18.251123923955397]
  Autoregressive learning has become a dominant approach for text-to-image generation, offering high efficiency and visual quality.<n>Existing watermarking methods, designed for diffusion models, often struggle to adapt to the sequential nature of VAR models.<n>We propose Safe- VAR, the first watermarking framework specifically designed for autoregressive text-to-image generation.
  arXiv  Detail & Related papers  (2025-03-14T11:45:10Z)
• SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution [27.345134138673945]
  We propose SuperMark, a robust, training-free watermarking framework.<n>SuperMark embeds the watermark into initial Gaussian noise using existing techniques.<n>It then applies pre-trained Super-Resolution models to denoise the watermarked noise, producing the final watermarked image.<n>For extraction, the process is reversed: the watermarked image is inverted back to the initial watermarked noise via DDIM Inversion, from which the embedded watermark is extracted.<n>Experiments demonstrate that SuperMark achieves fidelity comparable to existing methods while significantly improving robustness.
  arXiv  Detail & Related papers  (2024-12-13T11:20:59Z)
• OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking [20.662260046296897]
  Existing versatile watermarking approaches suffer from trade-offs between tamper localization precision and visual quality.<n>We propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction.<n>Our method outperforms it by 4.25dB in PSNR of the container image, 20.7% in F1-Score under noisy conditions, and 14.8% in average bit accuracy.
  arXiv  Detail & Related papers  (2024-12-02T15:38:44Z)
• Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances [13.746887960091112]
  Large-scale text-to-image models can distort embedded watermarks during editing, posing challenges to copyright protection.<n>We introduce W-Bench, the first comprehensive benchmark designed to evaluate the robustness of watermarking methods.<n>We propose VINE, a watermarking method that significantly enhances robustness against various image editing techniques.
  arXiv  Detail & Related papers  (2024-10-24T14:28:32Z)
• Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending [54.26862913139299]
  We introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB)<n> TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models.<n>Experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
  arXiv  Detail & Related papers  (2024-09-17T07:52:09Z)
• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• Diffusion-Based Hierarchical Image Steganography [60.69791384893602]
  Hierarchical Image Steganography is a novel method that enhances the security and capacity of embedding multiple images into a single container. It exploits the robustness of the Diffusion Model alongside the reversibility of the Flow Model. The innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text.
  arXiv  Detail & Related papers  (2024-05-19T11:29:52Z)
• WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
  WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness. We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests. We envision WAVES as a toolkit for the future development of robust watermarks.
  arXiv  Detail & Related papers  (2024-01-16T18:58:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.