Bi-Level Optimization for Self-Supervised AI-Generated Face Detection

Supervised learning has been the dominant approach for developing detectors of AI-generated face images. However, the reliance on pre-generated face samples often limits the adaptability to the diverse and rapidly evolving landscape of AI face generators. Here, we propose a bi-level optimization framework for self-supervised AI-generated face detection, relying solely on photographic images and aligning the pretext tasks with the downstream AI face detection. The inner loop optimization aims to train a feature extractor using linearly weighted objectives of several pretext tasks, including classifying categorical exchangeable image file format (EXIF) tags, ranking ordinal EXIF tags, and identifying global and local face manipulations. The outer loop optimization treats the coarse-grained detection of face manipulations as a surrogate task for AI-generated image detection, directing the feature extractor to adapt to detecting AI faces by optimizing the linear weightings to align the task relationships. To validate the effectiveness of our self-supervised features, we first frame AI-generated face detection as one-class classification, and model the feature distribution of photographic images using a Gaussian mixture model. Faces with low likelihoods are flagged as AI-generated. Additionally, we train a two-layer perceptron based on the extracted self-supervised features as a simple binary classifier. We demonstrate by comprehensive experiments that our AI-generated face detectors markedly advance the state-of-the-art across various generative models.