AdvBiom: Adversarial Attacks on Biometric Matchers
This work addresses security risks in biometric authentication systems, which is critical for applications in security and privacy, though it is incremental as it builds on existing adversarial attack methods.
The paper tackles the vulnerability of deep learning-based biometric systems, such as face recognition, by demonstrating that small, imperceptible changes can evade most existing systems, and it shows how a generator can be trained and extended to other modalities like fingerprint recognition.
With the advent of deep learning models, face recognition systems have achieved impressive recognition rates. The workhorses behind this success are Convolutional Neural Networks (CNNs) and the availability of large training datasets. However, we show that small human-imperceptible changes to face samples can evade most prevailing face recognition systems. Even more alarming is the fact that the same generator can be extended to other traits in the future. In this work, we present how such a generator can be trained and also extended to other biometric modalities, such as fingerprint recognition systems.