AUCが誤解を招くとき：ドメインシフト下でのディープフェイク検出器の分極化を考慮した評価
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
ディープフェイク検出器の汎化性能を評価する際、従来のAUCはデータソース混合やアーティファクト多様性を反映しないため、新指標Cross-AUCを提案し、ドメインシフト下での頑健性をより現実的に評価する。
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著者: Dat Nguyen, Cosmin Radoi, Romain Hermary, Marcella Astrid, Nesryne Mejri, Enjie Ghorbel, Djamila Aouada
分類: cs.CV, cs.LG
原文アブストラクト
Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including financial fraud and non-consensual explicit content. In response, deepfake detection has become an active research area, with recent methods increasingly focusing on improving generalization to unseen manipulations. This is typically evaluated using the Area Under the ROC Curve (AUC) measured separately across multiple datasets. However, such an evaluation fails to reflect real-world scenarios where detectors face a mixture of data sources and varying artifact types. To address this limitation, we introduce a novel metric, Cross-dataset AUC (Cross-AUC) that averages per-domain AUCs with a measure of prediction polarization for taking into account the robustness to domain shift. The polarization extent is quantified by the Wasserstein Distance between class score distributions. Cross-AUC not only assesses the generalization capabilities of deepfake detectors under domain shifts more realistically, but it is also interpretable as it better explains the reason behind a drop in performance. Experiments performed on seven benchmark datasets demonstrate its practical relevance.
