Além do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes
It addresses the need for more comprehensive evaluation methods in deepfake detection, which is crucial for combating fraud and misinformation, but is incremental as it builds on existing detection techniques.
This paper tackled the problem of evaluating deepfake detectors beyond classification performance by proposing a reliability assessment framework based on transferability, robustness, interpretability, and computational efficiency, revealing significant progress and critical limitations in five state-of-the-art methods.
Deepfakes are synthetic media generated by artificial intelligence, with positive applications in education and creativity, but also serious negative impacts such as fraud, misinformation, and privacy violations. Although detection techniques have advanced, comprehensive evaluation methods that go beyond classification performance remain lacking. This paper proposes a reliability assessment framework based on four pillars: transferability, robustness, interpretability, and computational efficiency. An analysis of five state-of-the-art methods revealed significant progress as well as critical limitations.