Empirical Study of Face Authentication Systems Under OSNFD Attacks

Face authentication has been widely available on smartphones, tablets, and laptops. As numerous personal images are published in online social networks (OSNs), OSN-based facial disclosure (OSNFD) creates significant threat against face authentication. We make the first attempt to quantitatively measure OSNFD threat to real-world face authentication systems on smartphones, tablets, and laptops. Our results show that the percentage of vulnerable users that are subject to spoofing attacks is high, which is about 64 percent for laptop users, and 93 percent smartphone/tablet users. We investigate liveness detection methods in the real-world face authentication systems against OSNFD threat. We discover that under protection of liveness detection, the percentage of vulnerable images is 18.8 percent, but the percentage of vulnerable users is as high as 73.3 percent. This evidence suggests that the current face authentication systems are not strong enough under OSNFD attacks. Finally, we develop a risk estimation tool based on logistic regression, and analyze the impacts of key attributes of facial images on the OSNFD risk. Our statistical analysis reveals that the most influential attributes of facial images are image resolution, facial makeup, occluded eyes, and illumination. This tool can be used to evaluate OSNFD risk for OSN images to increase users’ awareness of OSNFD.