Benchmarking Deepfake Attacks on Deep Face Recognition Systems

Modern deep face recognition systems are crucial for identity verification and authentication in high-stakes sectors such as security, finance, and law enforcement. However, their reliability is increasingly threatened by the rapid advancement of deepfake technology. Despite considerable attention, comprehensive evaluation of how emerging deepfake generation methods affect reliability of face recognition systems remains lacking. In this paper, we introduce a principled taxonomy and a benchmarking framework that structure deepfake attacks by intent and generative mechanism, enabling consistent and comprehensive assessment. Numerous results show that diverse deepfake attacks commonly exceed 70% success and, in some regimes, surpass 90%. Built on this foundation, we investigate the underlying mechanisms across different deepfake techniques, providing not only broad empirical evaluation but also valuable insight into what drives attack success. Our in-depth analysis reveals that success is not governed by visual quality, but rather by the degree of identity controllability.