IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI

• URL: http://arxiv.org/abs/2310.19248v1
• Date: Mon, 30 Oct 2023 03:33:41 GMT
• Title: IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
• Authors: Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li, Jinghui Chen
• Abstract summary: Diffusion-based image generation models can create artistic images that mimic the style of an artist or maliciously edit the original images for fake content. Several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations. In this work, we introduce a purification perturbation platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure.
• Score: 52.90082445349903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based image generation models, such as Stable Diffusion or DALL-E 2, are able to learn from given images and generate high-quality samples following the guidance from prompts. For instance, they can be used to create artistic images that mimic the style of an artist based on his/her original artworks or to maliciously edit the original images for fake content. However, such ability also brings serious ethical issues without proper authorization from the owner of the original images. In response, several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations, which are designed to mislead the diffusion model and make it unable to properly generate new samples. In this work, we introduce a perturbation purification platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure. IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e.g., style mimicking, malicious editing). The proposed IMPRESS platform offers a comprehensive evaluation of several contemporary protection methods, and can be used as an evaluation platform for future protection methods.

Related papers

• 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. In this paper, we examine the robustness of various watermark-based protection methods applied to text-to-image models.
  arXiv  Detail & Related papers  (2024-11-22T22:28:19Z)
• EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations [73.94175015918059]
  We introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing.
  arXiv  Detail & Related papers  (2024-06-20T02:02:44Z)
• Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model [61.53213964333474]
  We propose a unified framework Adv-Diffusion that can generate imperceptible adversarial identity perturbations in the latent space but not the raw pixel space. Specifically, we propose the identity-sensitive conditioned diffusion generative model to generate semantic perturbations in the surroundings. The designed adaptive strength-based adversarial perturbation algorithm can ensure both attack transferability and stealthiness.
  arXiv  Detail & Related papers  (2023-12-18T15:25:23Z)
• Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion? [21.75921532822961]
  We introduce a purification method capable of removing protected perturbations while preserving the original image structure. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.
  arXiv  Detail & Related papers  (2023-11-30T07:17:43Z)
• 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)
• 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)
• Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation [25.55296442023984]
  We propose a method, Unlearnable Diffusion Perturbation, to safeguard images from unauthorized exploitation. This achievement holds significant importance in real-world scenarios, as it contributes to the protection of privacy and copyright against AI-generated content.
  arXiv  Detail & Related papers  (2023-06-02T20:19:19Z)
• JPEG Compressed Images Can Bypass Protections Against AI Editing [48.340067730457584]
  Imperceptible perturbations have been proposed as a means of protecting images from malicious editing. We find that the aforementioned perturbations are not robust to JPEG compression.
  arXiv  Detail & Related papers  (2023-04-05T05:30:09Z)

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.