Generalizable Synthetic Image Detection via Language-guided Contrastive Learning

Haiwei Wu, Jiantao Zhou, Shile Zhang

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models. In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. We first augment the training images with carefully-designed textual labels, enabling us to use a joint image-text contrastive learning for the forensic feature extraction. In addition, we formulate the synthetic image detection as an identification problem, which is vastly different from the traditional classification-based approaches. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors by +22.66% accuracy and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED.

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