De-Fake：基于风格的异常深度伪造检测
De-Fake: Style based Anomaly Deepfake Detection
AI总结：
提出基于风格特征的深度伪造检测方法SafeVision，在不访问真实面部图像的前提下有效识别换脸伪造，同时提供隐私保护。
摘要：
检测涉及换脸的深度伪造面临重大挑战，尤其是在现实场景中，任何人都可以使用免费工具和应用进行换脸而无需任何技术知识。现有的深度伪造检测方法依赖于面部特征点或像素级特征的不一致性，在处理源面部无缝融合到目标图像或视频中的换脸深度伪造时往往效果不佳。换脸在日常生活中十分普遍，被用于传播虚假信息、损害声誉、操纵政治观点、制作非自愿亲密深度伪造（NCID），并通过制作儿童性虐待材料（CSAM）来剥削儿童。即使是知名公众人物也未能幸免，其大量深度伪造内容在社交媒体平台上广泛传播。深度伪造检测方法面临的另一个挑战是创建涵盖广泛变化的数据集，因为训练模型需要大量数据。这引发了隐私担忧，特别是关于个人面部数据的处理和存储，可能导致未经授权的访问或滥用。我们的核心思想是识别这些风格差异，以在不访问真实面部图像的情况下有效检测换脸图像。我们使用多个数据集和换脸方法进行了全面评估，展示了 SafeVision 在各种场景下检测换脸深度伪造的有效性。SafeVision 提供了一种可靠且可扩展的解决方案，能够以保护隐私的方式检测换脸，使其在具有挑战性的现实应用中尤为有效。据我们所知，SafeVision 是首个使用风格特征同时提供固有隐私保护的深度伪造检测方法。
英文摘要：
Detecting deepfakes involving face-swaps presents a significant challenge, particularly in real-world scenarios where anyone can perform face-swapping with freely available tools and apps without any technical knowledge. Existing deepfake detection methods rely on facial landmarks or inconsistencies in pixel-level features and often struggle with face-swap deepfakes, where the source face is seamlessly blended into the target image or video. The prevalence of face-swap is evident in everyday life, where it is used to spread false information, damage reputations, manipulate political opinions, create non-consensual intimate deepfakes (NCID), and exploit children by enabling the creation of child sexual abuse material (CSAM). Even prominent public figures are not immune to its impact, with numerous deepfakes of them circulating widely across social media platforms. Another challenge faced by deepfake detection methods is the creation of datasets that encompass a wide range of variations, as training models require substantial amounts of data. This raises privacy concerns, particularly regarding the processing and storage of personal facial data, which could lead to unauthorized access or misuse. Our key idea is to identify these style discrepancies to detect face-swapped images effectively without accessing the real facial image. We perform comprehensive evaluations using multiple datasets and face-swapping methods, which showcases the effectiveness of SafeVision in detecting face-swap deepfakes across diverse scenarios. SafeVision offers a reliable and scalable solution for detecting face-swaps in a privacy preserving manner, making it particularly effective in challenging real-world applications. To the best of our knowledge, SafeVision is the first deepfake detection using style features while providing inherent privacy protection.
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。
