Unlearnable Examples for Diffusion Models: Protect Data from
  Unauthorized Exploitation
Zhengyue Zhao Jinhao Duan Xing Hu Kaidi Xu Chenan Wang Rui Zhang Zidong Du Qi Guo Yunji Chen
Diffusion models have demonstrated remarkable performance in image generation
tasks, paving the way for powerful AIGC applications. However, these
widely-used generative models can also raise security and privacy concerns,
such as copyright infringement, and sensitive data leakage. To tackle these
issues, we propose a method, Unlearnable Diffusion Perturbation, to safeguard
images from unauthorized exploitation. Our approach involves designing an
algorithm to generate sample-wise perturbation noise for each image to be
protected. This imperceptible protective noise makes the data almost
unlearnable for diffusion models, i.e., diffusion models trained or fine-tuned
on the protected data cannot generate high-quality and diverse images related
to the protected training data. Theoretically, we frame this as a max-min
optimization problem and introduce EUDP, a noise scheduler-based method to
enhance the effectiveness of the protective noise. We evaluate our methods on
both Denoising Diffusion Probabilistic Model and Latent Diffusion Models,
demonstrating that training diffusion models on the protected data lead to a
significant reduction in the quality of the generated images. Especially, the
experimental results on Stable Diffusion demonstrate that our method
effectively safeguards images from being used to train Diffusion Models in
various tasks, such as training specific objects and styles. This achievement
holds significant importance in real-world scenarios, as it contributes to the
protection of privacy and copyright against AI-generated content.