Abstract
Biometrics have been widely applied as personally identifiable data for user authentication. However, existing biometric authentications are vulnerable to biometric spoofing. One reason is that they are easily observable and vulnerable to physical forgeries. Examples are the apparent surface patterns of human bodies, such as fingerprints and faces. A more significant issue is that existing authentication methods are entirely built upon biometric features, which almost never change and could be obtained or learned by an adversary such as human voices. To address this inherent security issue of biometric authentications, we propose a novel acoustically extracted hand-grip biometric, which is associated with every user’s hand geometry, body-fat ratio, and gripping strength; It is implicit and available whenever they grip a handheld device. Furthermore, we integrate a coding technique in the biometric acquisition process, which encodes static biometrics into dynamic biometric features to prevent data reuse. Additionally, this low-cost method can be deployed on any handheld device that has a speaker and a microphone. In particular, we develop a challenge-response biometric authentication system, which consists of a pair of biometric encoder and decoder. We encode the ultrasonic signal according to a challenge sequence and extract a distinct biometric code as the response for each session. We then decode the biometric code to verify the user by a convolutional neural network-based algorithm, which not only examines the coding correctness but also verifies the biometric features presented by each biometric digit. Furthermore, we investigate diverse acoustic attacks to our system, by respectively assuming an adversary could present the correct code, generate similar biometric features or successfully forge both. Extensive experiments on mobile devices show that our system achieves 97% accuracy to distinguish users and rejects 100% replay and synthesis attacks with 6-digit codes.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.