用自监督学习检测AI生成人脸，无需标注数据。
Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies
- 从照片中自学习相机和人脸特征，通过排序EXIF标签训练模型。
- 用高斯混合模型建模真实人脸特征分布，低概率即为可疑生成脸。
- 对新出现的生成器有强泛化能力，适合安全检测场景使用。
AI生成人脸的检测通常被当作二分类任务，但现有检测器难以适应快速演进的新生成技术。本文提出一种基于自监督学习的异常检测方法，仅利用真实照片人脸图像，学习相机内在特征与人脸特异性特征。核心是设计一个预训练任务：让模型对四个可交换的EXIF标签进行有序排列，并识别人为篡改的人脸图像。随后，采用高斯混合模型建模真实人脸特征分布，将似然值较低的样本标记为AI生成。定量与定性实验均验证了方法的有效性。代码已公开于https://github.com/MZMMSEC/AIGFD_EXIF.git。
原文摘要 · Abstract (English)
The detection of AI-generated faces is commonly approached as a binary classification task. Nevertheless, the resulting detectors frequently struggle to adapt to novel AI face generators, which evolve rapidly. In this paper, we describe an anomaly detection method for AI-generated faces by leveraging self-supervised learning of camera-intrinsic and face-specific features purely from photographic face images. The success of our method lies in designing a pretext task that trains a feature extractor to rank four ordinal exchangeable image file format (EXIF) tags and classify artificially manipulated face images. Subsequently, we model the learned feature distribution of photographic face images using a Gaussian mixture model. Faces with low likelihoods are flagged as AI-generated. Both quantitative and qualitative experiments validate the effectiveness of our method. Our code is available at \url{https://github.com/MZMMSEC/AIGFD_EXIF.git}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。