In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications. Despite the numerous benefits brought by recent advances in diffusion models, there are also concerns about their potential misuse, specifically in terms of privacy breaches and intellectual property infringement. In particular, some of their unique characteristics open up new attack surfaces when considering the real-world deployment of such models. With a thorough investigation of the attack vectors, we develop a systematic analysis of membership inference attacks on diffusion models and propose novel attack methods tailored to each attack scenario specifically relevant to diffusion models. Our approach exploits easily obtainable quantities and is highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in realistic scenarios. Our extensive experiments demonstrate the effectiveness of our method, highlighting the importance of considering privacy and intellectual property risks when using diffusion models in image generation tasks.
翻译:近年来,传播模式在图像生成领域取得了巨大成功,成为了以AI为基础的图像处理应用的最先进技术。尽管传播模式最近的进展带来了许多好处,但人们也担心这些模式可能被滥用,特别是在侵犯隐私和侵犯知识产权方面。特别是,这些模式的一些独特特点在考虑这些模型的真实世界部署情况时打开了新的攻击表面。通过对攻击矢量进行彻底调查,我们系统地分析对传播模式的会员推断攻击,并针对与传播模式具体相关的每一种攻击情景提出新的攻击方法。我们的方法利用了易于获取的数量,非常有效,在现实情景下实现了近乎完美的攻击性能(>0.9 AUCROC ) 。我们的广泛实验显示了我们的方法的有效性,突出了在利用传播模型进行图像生成任务时考虑隐私和知识产权风险的重要性。