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.

翻译：深度伪造是由人工智能生成的合成媒体，在教育与创意领域具有积极应用，但也带来欺诈、虚假信息传播及隐私侵犯等严重负面影响。尽管检测技术已取得进展，但目前仍缺乏超越分类性能的综合评估方法。本文提出基于四大支柱的可靠性评估框架：可迁移性、鲁棒性、可解释性与计算效率。通过对五种前沿方法的分析，揭示了显著进展与关键局限性。