Anti-Reference: Universal and Immediate Defense Against Reference-Based Generation

• URL: http://arxiv.org/abs/2412.05980v1
• Date: Sun, 08 Dec 2024 16:04:45 GMT
• Title: Anti-Reference: Universal and Immediate Defense Against Reference-Based Generation
• Authors: Yiren Song, Shengtao Lou, Xiaokang Liu, Hai Ci, Pei Yang, Jiaming Liu, Mike Zheng Shou,
• Abstract summary: Anti-Reference is a novel method that protects images from the threats posed by reference-based generation techniques. We propose a unified loss function that enables joint attacks on fine-tuning-based customization methods. Our method shows certain transfer attack capabilities, effectively challenging both gray-box models and some commercial APIs.
• Score: 24.381813317728195
• License:
• Abstract: Diffusion models have revolutionized generative modeling with their exceptional ability to produce high-fidelity images. However, misuse of such potent tools can lead to the creation of fake news or disturbing content targeting individuals, resulting in significant social harm. In this paper, we introduce Anti-Reference, a novel method that protects images from the threats posed by reference-based generation techniques by adding imperceptible adversarial noise to the images. We propose a unified loss function that enables joint attacks on fine-tuning-based customization methods, non-fine-tuning customization methods, and human-centric driving methods. Based on this loss, we train a Adversarial Noise Encoder to predict the noise or directly optimize the noise using the PGD method. Our method shows certain transfer attack capabilities, effectively challenging both gray-box models and some commercial APIs. Extensive experiments validate the performance of Anti-Reference, establishing a new benchmark in image security.

Related papers

• Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models [57.16056181201623]
  Fine-tuning text-to-image diffusion models can inadvertently undo safety measures, causing models to relearn harmful concepts. We present a novel but immediate solution called Modular LoRA, which involves training Safety Low-Rank Adaptation modules separately from Fine-Tuning LoRA components. This method effectively prevents the re-learning of harmful content without compromising the model's performance on new tasks.
  arXiv  Detail & Related papers  (2024-11-30T04:37:38Z)
• DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing [93.45507533317405]
  DiffusionGuard is a robust and effective defense method against unauthorized edits by diffusion-based image editing models. We introduce a novel objective that generates adversarial noise targeting the early stage of the diffusion process. We also introduce a mask-augmentation technique to enhance robustness against various masks during test time.
  arXiv  Detail & Related papers  (2024-10-08T05:19:19Z)
• High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image Synthesis [12.555117983678624]
  We propose a new adversarial attack method that adds strong perturbation on the high-frequency areas of images to make it more robust to adversarial purification. Our experiment showed that the adversarial images retained noise even after adversarial purification, hindering malicious image generation.
  arXiv  Detail & Related papers  (2024-09-12T15:58:28Z)
• 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)
• LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations [25.929492841042666]
  We present a novel approach to generate transferable targeted adversarial examples. We exploit the vulnerability of deep neural networks to perturbations on high-frequency components of images. Our proposed approach significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-31T04:54:55Z)
• IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI [52.90082445349903]
  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.
  arXiv  Detail & Related papers  (2023-10-30T03:33:41Z)
• 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)
• Guided Diffusion Model for Adversarial Purification [103.4596751105955]
  Adversarial attacks disturb deep neural networks (DNNs) in various algorithms and frameworks. We propose a novel purification approach, referred to as guided diffusion model for purification (GDMP) On our comprehensive experiments across various datasets, the proposed GDMP is shown to reduce the perturbations raised by adversarial attacks to a shallow range.
  arXiv  Detail & Related papers  (2022-05-30T10:11:15Z)
• Diffusion Models for Adversarial Purification [69.1882221038846]
  Adrial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. We propose DiffPure that uses diffusion models for adversarial purification. Our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods.
  arXiv  Detail & Related papers  (2022-05-16T06:03:00Z)

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.