Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending

• URL: http://arxiv.org/abs/2409.10958v2
• Date: Mon, 16 Dec 2024 02:37:33 GMT
• Title: Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
• Authors: Yongyang Pan, Xiaohong Liu, Siqi Luo, Yi Xin, Xiao Guo, Xiaoming Liu, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: 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.
• Score: 54.26862913139299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.

Related papers

• GenPTW: In-Generation Image Watermarking for Provenance Tracing and Tamper Localization [32.843425702098116]
  GenPTW is an In-Generation image watermarking framework for latent diffusion models (LDMs) It embeds structured watermark signals during the image generation phase, enabling unified provenance tracing and tamper localization. Experiments demonstrate that GenPTW outperforms existing methods in image fidelity, watermark extraction accuracy, and tamper localization performance.
  arXiv  Detail & Related papers  (2025-04-28T08:21:39Z)
• 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. We propose a diffusion model watermarking method tailored for real-world deployment. 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. Existing watermarking methods, designed for diffusion models, often struggle to adapt to the sequential nature of VAR models. 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)
• Dynamic watermarks in images generated by diffusion models [46.1135899490656]
  High-fidelity text-to-image diffusion models have revolutionized visual content generation, but their widespread use raises significant ethical concerns. We propose a novel multi-stage watermarking framework for diffusion models, designed to establish copyright and trace generated images back to their source. Our work advances the field of AI-generated content security by providing a scalable solution for model ownership verification and misuse prevention.
  arXiv  Detail & Related papers  (2025-02-13T03:23:17Z)
• Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory [33.78620829249978]
  Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images. Recent attention-based methods have improved object inclusion and linguistic binding, but still face challenges such as attribute misbinding. We propose a Bayesian approach that designs custom priors over attention distributions to enforce desirable properties. Our approach treats the attention mechanism as an interpretable component, enabling fine-grained control and improved attribute-object alignment.
  arXiv  Detail & Related papers  (2024-11-25T10:57:48Z)
• An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation [21.058231817498115]
  We propose an efficient watermarking method for latent diffusion models (LDMs) based on Low-Rank Adaptation (LoRA) We show that the proposed method ensures fast watermark embedding and maintains a very low bit error rate of the watermark, a high-quality of the generated image, and a zero false negative rate (FNR) for verification.
  arXiv  Detail & Related papers  (2024-10-26T15:23:49Z)
• ZePo: Zero-Shot Portrait Stylization with Faster Sampling [61.14140480095604]
  This paper presents an inversion-free portrait stylization framework based on diffusion models that accomplishes content and style feature fusion in merely four sampling steps. We propose a feature merging strategy to amalgamate redundant features in Consistency Features, thereby reducing the computational load of attention control.
  arXiv  Detail & Related papers  (2024-08-10T08:53:41Z)
• Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking [20.320229647850017]
  Stable diffusion (SD) models have typically flourished in the field of image synthesis and personalized editing. The exposure of AI-created content on public platforms could raise both legal and ethical risks. In this work, we propose a Safe and high-traceable Stable Diffusion framework (namely SafeSD) to adaptive implant the watermarks into the imperceptible structure.
  arXiv  Detail & Related papers  (2024-07-18T05:53:17Z)
• JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits [76.25962336540226]
  JIGMARK is a first-of-its-kind watermarking technique that enhances robustness through contrastive learning. Our evaluation reveals that JIGMARK significantly surpasses existing watermarking solutions in resilience to diffusion-model edits.
  arXiv  Detail & Related papers  (2024-06-06T03:31:41Z)
• 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)
• AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA [67.68750063537482]
  Diffusion models have achieved remarkable success in generating high-quality images. Recent works aim to let SD models output watermarked content for post-hoc forensics. We propose textttmethod as the first implementation under this scenario.
  arXiv  Detail & Related papers  (2024-05-18T01:25:47Z)
• FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models [64.89896692649589]
  We propose FT-Shield, a watermarking solution tailored for the fine-tuning of text-to-image diffusion models. FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies.
  arXiv  Detail & Related papers  (2023-10-03T19:50:08Z)

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.