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Deepfake Detector Generalization Under Fake-Generation Evolution: A Controlled Distribution-Shift Study

Year: 2026

Type: preprint

Abstract: This paper investigates the generalization of deepfake detectors under controlled fake-generation distribution shift. Two paired datasets derived from FaceForensics++ are used: a benchmark-era dataset using Deepfakes face swapping and a diffusion-era dataset using Stable Diffusion face-region inpainting, while holding the real-image distribution constant. Xception and DINOv2-based detectors are evaluated using a 2×2 train/test protocol to measure both in-domain and cross-domain performance. Results show strong in-domain performance but substantial degradation under cross-domain evaluation, demonstrating that benchmark-only evaluation can overestimate robustness to evolving manipulation methods.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22233465

Authors: Hammad, Amro T, Hammad, Mahmoud T, Albarari, Mallak Mohammad, Al-Khanji, Raneem

Institution: Al Hussein Technical University

Language: English

Topic: Digital Media Forensic Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7204890690

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