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The aim is to enhance deepfake detection by developing strategies that handle out-of-distribution data effectively.

Enhancing abnormality identification: Robust out-of-distribution strategies for deepfake detection

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The aim is to enhance deepfake detection by developing strategies that handle out-of-distribution data effectively.
Proposed two novel Out-Of-Distribution detection approaches.
First approach focuses on reconstructing input images.
Second approach utilizes an attention mechanism for OOD detection.
Performed experiments to compare the proposed approaches with existing techniques.
Achieved promising results in deepfake detection.
Ranked among top-performing configurations on the benchmark.
Validated effectiveness of both proposed detection methods.

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Novel approaches enhance deepfake detection in open-set scenarios, suggesting improved adaptability for real-world applications.

Maiano et al. (2026) studied this question.

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