  Discerning between authentic content and that generated by advanced AI
methods has become increasingly challenging. While previous research primarily
addresses the detection of fake faces, the identification of generated natural
images has only recently surfaced. This prompted the recent exploration of
solutions that employ foundation vision-and-language models, like CLIP.
However, the CLIP embedding space is optimized for global image-to-text
alignment and is not inherently designed for deepfake detection, neglecting the
potential benefits of tailored training and local image features. In this
study, we propose CoDE (Contrastive Deepfake Embeddings), a novel embedding
space specifically designed for deepfake detection. CoDE is trained via
contrastive learning by additionally enforcing global-local similarities. To
sustain the training of our model, we generate a comprehensive dataset that
focuses on images generated by diffusion models and encompasses a collection of
9.2 million images produced by using four different generators. Experimental
results demonstrate that CoDE achieves state-of-the-art accuracy on the newly
collected dataset, while also showing excellent generalization capabilities to
unseen image generators. Our source code, trained models, and collected dataset
are publicly available at: https://github.com/aimagelab/CoDE.
