Multimedia Tools and Applications (Springer)
Our paper “DeepFeatureX-SN: Generalization of Deepfake Detection via Contrastive Learning” has been accepted for publication in Multimedia Tools and Applications (Springer)!
In this work, we introduce DeepFeatureX-SN, a novel deep learning model based on Siamese networks and contrastive learning designed to detect both GAN and Diffusion-based synthetic images. The architecture leverages three specialized feature extractors (for real, GAN, and DM-generated images), whose outputs are fused into a robust CNN classifier.
Highlights:-
State-of-the-art performance on generalization tasks involving unseen generative models.
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Strong robustness to image manipulations (JPEG compression, rotation, resizing).
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Outperforms existing deepfake detection methods across multiple benchmarks.
Authors: Orazio Pontorno, Luca Guarnera, Sebastiano Battiato
Code and dataset will be released soon at: https://github.com/mfs-iplab/DeepFeatureX-SN