Fingerprint authentication is widely adopted in modern identity verification systems due to its efficiency and cost-effectiveness. However, its extensive use poses significant risks, as fingerprint leakage could lead to sensitive information theft, severe financial and personnel losses, and even threats to national security. MasterPrint, which can accidentally match a significant proportion of fingerprint populations, underscores the vulnerabilities in fingerprint authentication systems. This paper introduces PrintListener++, a novel side-channel attack targeting Automatic Fingerprint Identification Systems (AFIS). PrintListener++ extracts first-level fingerprint pattern features from users’ fingertip-swiping actions on the touchscreens and synthesizes stronger targeted MasterPrints by integrating potential second-level features. Based on the generated MasterPrint templates, PrintListener++ reconstructs realistic fingerprint images, further expanding its potential threat. This attack method is highly covert and versatile, requiring only fingertip friction sound recordings, which can be easily obtained through social media platforms. Extensive real-world experiments demonstrate that PrintListener++ significantly enhances the attack potency of MasterPrint, raising critical security concerns for fingerprint authentication systems.

Pattern lock is widely used for user authentication in mobile devices due to its simplicity and ease of remembering. However, it is vulnerable to various attacks,e.g., shoulder surfing attacks. In this paper, we propose FingerPattern, a novel enhanced pattern lock authentication system by using friction sound as a second authentication factor. When the user inputs the pattern by swiping his/her fingertip on the screen, the friction sound is generated based on the user's fingerprint and is unique. Thus, FingerPattern can identify and rule out the illegality by utilizing fingerprint-dependent friction sound even if the adversary has inferred the pattern. By this method, FingerPattern secures pattern lock without the need for a change in user unlocking habits. Extensive experiments demonstrate that FingerPattern can identify legitimate users with 97.5% TAR in one attempt and defend against various attacks (e.g., only 16.8% FAR in five attempts even if the adversary can clearly spy on the user's unlocking process). FingerPattern can be incorporated into the existing pattern lock of mobile devices, which is cost-free. Furthermore, a user experience study shows that FingerPattern is well-received by users.