FaceShield：防御面部图像 against 深度伪造威胁
FaceShield: Defending Facial Image against Deepfake Threats
AI总结：
FaceShield通过操控扩散模型的注意力机制和面部特征提取器，提出了一种主动防御深度伪造的方案，有效提升对抗性扰动的鲁棒性和不可察觉性。
摘要：
随着深度伪造在犯罪活动中的广泛应用，这一现象引发了广泛关注的争议。尽管已有大量研究针对该问题，但大多数研究主要集中在深度伪造检测上。这些反应性解决方案作为犯罪中忽视真实性问题的根本方法是不够的。现有的主动防御措施也存在局限性，因为它们只对基于特定生成对抗网络（GANs）的深度伪造模型有效，难以应对近期扩散模型的进展。在本文中，我们提出了一种主动防御方法，名为FaceShield，它引入了针对由扩散模型（DMs）生成的深度伪造的新防御策略，并通过面部特征提取器的操控来防御各种现有的基于GAN的深度伪造模型。我们的方法包含三个主要组成部分：（i）操控DMs的注意力机制，在去噪过程中排除受保护的面部特征；（ii）针对显著的面部特征提取模型，以增强我们对抗性扰动的鲁棒性；（iii）利用高斯模糊和低通滤波技术来提高不可察觉性，同时增强对JPEG压缩的鲁棒性。在CelebA-HQ和VGGFace2-HQ数据集上的实验结果表明，我们的方法在针对最新的基于DMs的深度伪造模型方面取得了最先进的性能，同时也能转移到GANs，并展示了噪声的更高不可察觉性和增强的鲁棒性。代码可在此处获得：https://github.com/kuai-lab/iccv25_faceshield
英文摘要：
The rising use of deepfakes in criminal activities presents a significant issue, inciting widespread controversy. While numerous studies have tackled this problem, most primarily focus on deepfake detection. These reactive solutions are insufficient as a fundamental approach for crimes where authenticity is disregarded. Existing proactive defenses also have limitations, as they are effective only for deepfake models based on specific Generative Adversarial Networks (GANs), making them less applicable in light of recent advancements in diffusion-based models. In this paper, we propose a proactive defense method named FaceShield, which introduces novel defense strategies targeting deepfakes generated by Diffusion Models (DMs) and facilitates defenses on various existing GAN-based deepfake models through facial feature extractor manipulations. Our approach consists of three main components: (i) manipulating the attention mechanism of DMs to exclude protected facial features during the denoising process, (ii) targeting prominent facial feature extraction models to enhance the robustness of our adversarial perturbation, and (iii) employing Gaussian blur and low-pass filtering techniques to improve imperceptibility while enhancing robustness against JPEG compression. Experimental results on the CelebA-HQ and VGGFace2-HQ datasets demonstrate that our method achieves state-of-the-art performance against the latest deepfake models based on DMs, while also exhibiting transferability to GANs and showcasing greater imperceptibility of noise along with enhanced robustness. Code is available here: https://github.com/kuai-lab/iccv25_faceshield
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
