Inverting visual representations within deep neural networks (DNNs) presents a challenging and important problem in the field of security and privacy for deep learning. The main goal is to invert the features of an unidentified target image generated by a pre-trained DNN, aiming to reconstruct the original image. Feature inversion holds particular significance in understanding the privacy leakage inherent in contemporary split DNN execution techniques, as well as in various applications based on the extracted DNN features. In this paper, we explore the use of diffusion models, a promising technique for image synthesis, to enhance feature inversion quality. We also investigate the potential of incorporating alternative forms of prior knowledge, such as textual prompts and cross-frame temporal correlations, to further improve the quality of inverted features. Our findings reveal that diffusion models can effectively leverage hidden information from the DNN features, resulting in superior reconstruction performance compared to previous methods. This research offers valuable insights into how diffusion models can enhance privacy and security within applications that are reliant on DNN features.

本研究解决了深度神经网络中视觉表示的反演问题，这对深度学习的安全和隐私至关重要。作者提出利用扩散模型改善特征反演质量，并引入替代先验知识如文本提示和跨帧时间相关性，显著提升了重建效果。这项研究为利用扩散模型增强依赖DNN特征的应用的隐私和安全提供了有价值的见解。

揭示视觉秘密：利用扩散先验反演特征进行图像重建