Hey That's Mine Imperceptible Watermarks are Preserved in Diffusion Generated Outputs

• URL: http://arxiv.org/abs/2308.11123v2
• Date: Thu, 9 Nov 2023 03:39:59 GMT
• Title: Hey That's Mine Imperceptible Watermarks are Preserved in Diffusion Generated Outputs
• Authors: Luke Ditria, Tom Drummond
• Abstract summary: We show that a generative Diffusion model trained on data that has been imperceptibly watermarked will generate new images with these watermarks present. Our system offers a solution to protect intellectual property when sharing content online.
• Score: 12.763826933561244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have seen an explosion in popularity with the release of huge generative Diffusion models like Midjourney and Stable Diffusion to the public. Because of this new ease of access, questions surrounding the automated collection of data and issues regarding content ownership have started to build. In this paper we present new work which aims to provide ways of protecting content when shared to the public. We show that a generative Diffusion model trained on data that has been imperceptibly watermarked will generate new images with these watermarks present. We further show that if a given watermark is correlated with a certain feature of the training data, the generated images will also have this correlation. Using statistical tests we show that we are able to determine whether a model has been trained on marked data, and what data was marked. As a result our system offers a solution to protect intellectual property when sharing content online.

Related papers

• 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)
• Spread them Apart: Towards Robust Watermarking of Generated Content [4.332441337407564]
  We propose an approach to embed watermarks into the generated content to allow future detection of the generated content and identification of the user who generated it. We prove that watermarks embedded are guaranteed to be robust against additive perturbations of a bounded magnitude.
  arXiv  Detail & Related papers  (2025-02-11T09:23:38Z)
• RoboSignature: Robust Signature and Watermarking on Network Attacks [0.5461938536945723]
  We present a novel adversarial fine-tuning attack that disrupts the model's ability to embed the intended watermark. Our findings emphasize the importance of anticipating and defending against potential vulnerabilities in generative systems.
  arXiv  Detail & Related papers  (2024-12-22T04:36:27Z)
• SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models [77.80595722480074]
  SleeperMark is a novel framework designed to embed resilient watermarks into T2I diffusion models. It guides the model to disentangle the watermark information from the semantic concepts it learns, allowing the model to retain the embedded watermark. Our experiments demonstrate the effectiveness of SleeperMark across various types of diffusion models.
  arXiv  Detail & Related papers  (2024-12-06T08:44:18Z)
• Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage [14.985938758090763]
  Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. Recent studies highlight concerns over the use of unauthorized data in training these models, which may lead to intellectual property infringement or privacy violations. We propose RATTAN, that leverages the diffusion process to conduct controlled image generation on the protected input.
  arXiv  Detail & Related papers  (2024-11-22T22:28:19Z)
• 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)
• A Watermark-Conditioned Diffusion Model for IP Protection [31.969286898467985]
  We propose a unified watermarking framework for content copyright protection within the context of diffusion models. To tackle this challenge, we propose a Watermark-conditioned Diffusion model called WaDiff. Our method is effective and robust in both the detection and owner identification tasks.
  arXiv  Detail & Related papers  (2024-03-16T11:08:15Z)
• A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models [52.49582606341111]
  Copyright law confers creators the exclusive rights to reproduce, distribute, and monetize their creative works. Recent progress in text-to-image generation has introduced formidable challenges to copyright enforcement. We introduce a novel pipeline that harmonizes CLIP, ChatGPT, and diffusion models to curate a dataset.
  arXiv  Detail & Related papers  (2024-01-04T11:14:01Z)
• DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models [79.71665540122498]
  We propose a method for detecting unauthorized data usage by planting the injected content into the protected dataset. Specifically, we modify the protected images by adding unique contents on these images using stealthy image warping functions. By analyzing whether the model has memorized the injected content, we can detect models that had illegally utilized the unauthorized data.
  arXiv  Detail & Related papers  (2023-07-06T16:27:39Z)
• Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking [54.40184736491652]
  We propose a backdoor-based watermarking approach that serves as a general framework for safeguarding public-available data. By inserting a small number of watermarking samples into the dataset, our approach enables the learning model to implicitly learn a secret function set by defenders. This hidden function can then be used as a watermark to track down third-party models that use the dataset illegally.
  arXiv  Detail & Related papers  (2023-03-20T21:54:30Z)

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.