Gradient leakage has been identified as a potential source of privacy breaches in modern image processing systems, where the adversary can completely reconstruct the training images from leaked gradients. However, existing methods are restricted to reconstructing low-resolution images where data leakage risks of image processing systems are not sufficiently explored. In this paper, by exploiting diffusion models, we propose an innovative gradient-guided fine-tuning method and introduce a new reconstruction attack that is capable of stealing private, high-resolution images from image processing systems through leaked gradients where severe data leakage encounters. Our attack method is easy to implement and requires little prior knowledge. The experimental results indicate that current reconstruction attacks can steal images only up to a resolution of $128 \times 128$ pixels, while our attack method can successfully recover and steal images with resolutions up to $512 \times 512$ pixels. Our attack method significantly outperforms the SOTA attack baselines in terms of both pixel-wise accuracy and time efficiency of image reconstruction. Furthermore, our attack can render differential privacy ineffective to some extent.

通过扩散模型并利用梯度指导的微调方法，我们提出了一种新的重建攻击方法，可通过泄露的梯度从图像处理系统中窃取私密的高分辨率图像。实验结果表明，我们的攻击方法能够成功恢复和窃取分辨率高达512 x 512像素的图像。此外，我们的攻击方法在图像重建的像素级准确性和时间效率方面明显优于其他攻击基准方法，并在一定程度上使差分隐私失效。

扩散模型的安全性研究：基于梯度引导的扩散模型导致严重数据泄漏