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Evaluation of High-Frequency Information for Deepfake Detection Using a CNN–ViT Architecture in a Cross-Dataset Scenario
Authors: Erikson E. Ferreira, Samla A. Tavares, Kelly S. A. Gazzoli, Karin S. Komatti
SIBGRAPI • 2026
With the advancement of deep learning techniques, the production of deepfake media has become increasingly sophisticated, raising the difficulty of detection. One of the main challenges is the low generalization capacity of models, which frequently show performance drops in scenarios different from those used in training. This work investigates the use of high-frequency filters Spatial Rich Models (SRM) and Sobel integrated into the CrossViT architecture to enhance deepfake detection. The models were trained on a subset of the Deepfake Detection Challenge (DFDC) dataset and evaluated under both in-dataset (DFDC) and cross-dataset (Celeb-DF v2 and FaceForensics++) scenarios. The results indicate that high-frequency filters consistently improve the performance of the methods, with the SRM applied to the L-Branch reaching the best metrics in the in-database DFDC scenario (AUC of 0.847) and in the cross-database Celeb-DF v2 scenario (AUC of 0.755), while providing SRM-filtered images to both CrossViT branches achieved the best result on FaceForensics++ (average AUC of 0.755).
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