This attack exploits the sound produced by a user's finger swiping across a touchscreen to extract fingerprint pattern details. Through testing, the researchers claim success rates of attacking "up to 27.9% of partial fingerprints and 9.3% of complete fingerprints within five attempts at the highest security FAR [False Acceptance Rate] setting of 0.01%." This research marks the first instance of utilizing swiping sounds to deduce fingerprint information.

Fingerprint biometric security measures are prevalent and widely trusted, with projections suggesting the fingerprint authentication market could reach nearly $100 billion by 2032. However, with growing awareness of potential fingerprint theft, individuals and organizations are becoming more cautious about exposing their fingerprints, even in photographs.

In the absence of direct access to fingerprints or detailed finger images, attackers have found a new avenue for obtaining fingerprint data to bolster dictionary attacks like MasterPrint and DeepMasterPrint. The PrintListener study reveals that "finger-swiping friction sounds can be captured by attackers online with a high possibility," using common communication apps such as Discord, Skype, WeChat, and FaceTime. By exploiting these sounds, the researchers developed PrintListener, a sophisticated attack method.

PrintListener overcomes significant challenges, including capturing faint friction sounds, separating fingerprint influences from other user characteristics, and advancing from primary to secondary fingerprint features. The researchers achieved this through the development of algorithms for sound localization, feature extraction, and statistical analysis.

Through extensive real-world experiments, PrintListener demonstrates remarkable success rates in compromising fingerprint security, surpassing unassisted dictionary attacks. This research underscores the importance of addressing emerging threats to biometric authentication systems and developing robust countermeasures to safeguard sensitive data.