On the Resilience of Biometric Authentication Systems against Random Inputs
arXiv:2001.04056 · doi:10.14722/ndss.2020.24210
Abstract
We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.
arXiv commentAccepted by NDSS2020, 18 pages
References in corpus (15)
- Scikit-learn: Machine Learning in Python
- VoxCeleb2: Deep Speaker Recognition
- VoxCeleb: a large-scale speaker identification dataset
- Learning Face Representation from Scratch
- Towards Deep Learning Models Resistant to Adversarial Attacks
- Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
- Stealing Machine Learning Models via Prediction APIs
- Certified Adversarial Robustness via Randomized Smoothing
- VGGFace2: A dataset for recognising faces across pose and age
- Tacotron: Towards End-to-End Speech Synthesis
- Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
- Classification regions of deep neural networks
- TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
- Explainable Black-Box Attacks Against Model-based Authentication
- Shattered Trust: When Replacement Smartphone Components Attack