Abstract
AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators.
Abstract (translated)
AI生成的面部检测器通过监督学习训练时，通常依赖于特定生成器合成的图像，这限制了它们对新兴生成技术的泛化能力。为克服这一局限性，我们提出了一种基于双层优化的自监督方法。在内部循环中，我们仅使用一组线性加权预设任务（如EXIF标签分类、顺序EXIF标签排序以及人工面部操作检测）对摄影人脸图像进行视觉编码器的预训练。外部循环则通过优化这些预设任务的相对权重来增强粗粒度的人脸篡改检测能力，作为识别AI生成面孔的代理任务，从而更紧密地将自监督学习与AI生成面部检测的最终目标相结合。完成预训练后，编码器保持固定状态不变，然后可以通过高斯混合模型适应摄影人脸特征下的异常检测或使用轻量级两层感知机进行二元分类来识别AI生成的人脸。 广泛的实验表明，在单类和二元分类设置下，我们的探测器在未见过的生成器上表现出显著优于现有方法的泛化能力。
URL
https://arxiv.org/abs/2507.22824
