FingerFaker: Spoofing Attack on COTS Fingerprint Recognition Without Victim's Knowledge

Fingerprint recognition has been a vital security guard for various applications whose vulnerability has been explored by different works. However, previous works on spoofing fingerprint recognition rely on prior knowledge (e.g., photos and minutiae) of the target fingerprint, which fails to implement in practical scenarios. In this paper, we design a fingerprint spoofing attack, namely FingerFaker, to explore the vulnerability of fingerprint recognition, which can spoof automated fingerprint recognition systems (AFRSs) without prior knowledge of target fingerprints. Specifically, we propose a novel concept of "pseudo-minutiae-set"as an effective optimization object and design a two-stage scheme to optimize "pseudo-minutiaeset"leveraging a two-factor evolutionary strategy. In addition, we use a GAN-based training strategy with a minutiae loss function to pre-train a fingerprint generator to map a "pseudo-minutiae-set"into a fingerprint. We use 6342 fingerprint images to verify the performance of FingerFaker on spoofing the open-source AFRS, which shows a high attack success rate (ASR) of 97.78%. Meanwhile, we conduct a realistic case study on commercial off-the-shelf (COTS) AFRS, where FingerFaker also shows 94.22% ASR. Finally, we explore the impact of different conditions to guide the attack and propose countermeasures to mitigate the harm.