A Study of Training Strategies on Enhancing Human Detection of AI-Synthesized Faces

Abstract

Artificial intelligence (AI) synthesized faces—so called deepfake images—have been increasingly used for malicious intent and have resulted in prominently adverse impact. Because online users must contend with discerning fake from real, great emphasis has been placed on enhancing human detection of deepfake images. We conducted an online human-subject study (N=237), investigating the effect of three training strategies (explicit training with visible artifacts in synthetic faces, implicit training with experiencing the generation of synthetic faces using real human faces, and a combination of both artifact and generation) on participants’ detection of synthetic faces generated by the state-of-the-art StyleGAN techniques. Comparing participants’ deepfake detection across three phases (baseline in phase 1 without any training, phase 2 after one training session, and phase 3 after the other training session), we found that all training strategies effectively enhanced participants’ detection of AI-synthesized faces and their decision confidence. We also explored factors that impact participants’ learning and decision-making of deepfake detection. Responses to the open-ended question revealed that participants developed generalized strategies and utilized artifacts beyond the training. Our quantitative and qualitative results provide nuanced insights into the promises and limitations of the training strategies. In addition to advancing theoretical understanding of human training in the context of deepfake image detection, our study findings hold practical implications for interface design.