Boosting Geometric Invariants for Discriminative Forensics of Large-Scale Generated Visual Content

Generative artificial intelligence has shown great success in visual content synthesis such that humans struggle to distinguish between real and synthesized images. Forensic research seeks to reveal artifacts in such generated images, ensuring information security or improving generation capability. In this regard, the robustness and interpretability are important for the trustworthy purpose of forensic tasks. However, typical forensic models and their underlying data representations rely on empirical learning algorithms, which cannot effectively handle the high robustness and interpretability requirements beyond experience. As an effective solution, we extend the classical geometric invariants to the forensic research of large-scale generated images. Invariants are handcrafted representations with robust and interpretable geometric principles. However, their discriminability is far from the large scale of today's forensic tasks. We boost the discriminability by extending the classical invariants to the hierarchical architecture of convolutional neural networks. The resulting overcompleteness allows for an automatic selection of task-discriminative features, while retaining the previous advantages of robustness and interpretability. From generative adversarial networks to diffusion models, the forensic with our boosted invariants demonstrates state-of-the-art discriminability against large-scale content diversity. It also exhibits high efficiency on training examples, intrinsic invariance to geometric variations, and better interpretability of the forensic process.