Abstract
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.
Abstract (translated)
检测涉及面部交换的深度伪造（Deepfakes）在现实场景中面临巨大挑战，因为任何人都可以使用免费工具和应用程序进行面部交换，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不一致来识别，但对于无缝融合源面孔到目标图像或视频中的面部交换深度伪造，这些方法往往效果不佳。在日常生活中，这种类型的面部交换被用于传播虚假信息、损害声誉、操纵政治观点、创建非自愿亲密深度伪造（NCID）以及通过允许生成儿童性虐待材料（CSAM）来剥削儿童。即使是知名公众人物也无法幸免于其影响，在社交媒体平台上广泛流传着他们的许多深度伪造作品。另一个挑战是为涵盖各种变化的训练模型创建数据集，这引发了有关处理和存储个人面部数据的隐私担忧，可能会导致未经授权访问或滥用。 我们的主要想法是在不获取真实面部图像的情况下，通过识别这些风格差异来有效检测面部交换图片。我们使用多种数据集和面部交换方法进行了全面评估，展示了SafeVision在各种场景下检测面部交换深度伪造的有效性。SafeVision提供了一种可靠且可扩展的解决方案，在保护隐私的同时检测面部交换，使其特别适用于具有挑战性的现实应用。 据我们所知，SafeVision是第一个利用风格特征进行深度伪造检测的方法，并提供了固有的隐私保护功能。
URL
https://arxiv.org/abs/2507.03334
