评估深度伪造检测器的可靠性，超越准确率
Além do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes
- 从可迁移性、鲁棒性等四方面构建可靠性评估框架
- 测试五种顶尖检测方法，发现性能提升但存在明显短板
- 适合关注检测器真实可信度的研究者和应用开发者
深度伪造是人工智能生成的合成媒体，虽在教育与创作中有积极应用，但也带来欺诈、虚假信息和隐私侵犯等严重问题。尽管检测技术不断进步，但超越分类性能的综合性评估方法仍显不足。本文提出基于可迁移性、鲁棒性、可解释性和计算效率四个维度的可靠性评估框架。对五种前沿检测方法的分析表明，虽然整体进展显著，但仍存在关键局限。
原文摘要 · Abstract (English)
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.
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