Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation

• URL: http://arxiv.org/abs/2412.18791v1
• Date: Wed, 25 Dec 2024 06:06:41 GMT
• Title: Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation
• Authors: Sen Peng, Jijia Yang, Mingyue Wang, Jianfei He, Xiaohua Jia,
• Abstract summary: Diffusion-based text-to-image models have shown immense potential for various image-related tasks. customizing these models using unauthorized data brings serious privacy and intellectual property issues. Existing methods introduce protective perturbations based on adversarial attacks. We present a survey of protective perturbation methods designed to prevent unauthorized data usage in diffusion-based image generation.
• Score: 15.363134355805764
• License:
• Abstract: Diffusion-based text-to-image models have shown immense potential for various image-related tasks. However, despite their prominence and popularity, customizing these models using unauthorized data also brings serious privacy and intellectual property issues. Existing methods introduce protective perturbations based on adversarial attacks, which are applied to the customization samples. In this systematization of knowledge, we present a comprehensive survey of protective perturbation methods designed to prevent unauthorized data usage in diffusion-based image generation. We establish the threat model and categorize the downstream tasks relevant to these methods, providing a detailed analysis of their designs. We also propose a completed evaluation framework for these perturbation techniques, aiming to advance research in this field.

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