Benjamin Zi Hao Zhao (University of New South Wales and Data61 CSIRO), Hassan Jameel Asghar (Macquarie University and Data61 CSIRO), Mohamed Ali Kaafar (Macquarie University and Data61 CSIRO)

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 emph{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.

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William Blair (Boston University), Andrea Mambretti (Northeastern University), Sajjad Arshad (Northeastern University), Michael Weissbacher (Northeastern University), William Robertson (Northeastern University), Engin Kirda (Northeastern University), Manuel Egele (Boston University)

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Haohuang Wen (The Ohio State University), Qingchuan Zhao (The Ohio State University), Qi Alfred Chen (University of California, Irvine), Zhiqiang Lin (The Ohio State University)

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Faysal Hossain Shezan (University of Virginia), Kaiming Cheng (University of Virginia), Zhen Zhang (Johns Hopkins University), Yinzhi Cao (Johns Hopkins University), Yuan Tian (University of Virginia)

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Bobtail: Improved Blockchain Security with Low-Variance Mining

George Bissias (University of Massachusetts Amherst), Brian N. Levine (University of Massachusetts Amherst)

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