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Robust Deepfake On Unrestricted Media: Generation And Detection

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arxiv 2202.06228 v1 pith:FBYKWR3N submitted 2022-02-13 cs.CV

Trung-Nghia Le , Huy H Nguyen , Junichi Yamagishi , Isao Echizen This is my paper · ORCID
classification cs.CV
keywords deepfakemediadetectiongenerationfakewideacademicadvances
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Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also leads to serious social and criminal concerns. This chapter explores the evolution of and challenges in deepfake generation and detection. It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos). Finally, it suggests a focus for future fake media research.

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  1. Do DeepFake Attribution Models Generalize?

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Binary DeepFake detectors generalize across datasets better than multi-class attribution models, and attribution models degrade sharply even for manipulation methods seen during training.

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