Xiang Cai

Xiaochun Lei, Chang Lu, Zetao Jiang et al.

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch shrinks during preprocessing, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.

Xiang Cai, Rishab Nithyanand, Rob Johnson

Website fingerprinting attacks enable an adversary to infer which website a victim is visiting, even if the victim uses an encrypting proxy, such as Tor. Previous work has shown that all proposed defenses against website fingerprinting attacks are ineffective. This paper advances the study of website fingerprinting attacks and defenses in two ways. First, we develop bounds on the trade-off between security and bandwidth overhead that any fingerprinting defense scheme can achieve. This enables us to compare schemes with different security/overhead trade-offs by comparing how close they are to the lower bound. We then refine, implement, and evaluate the Congestion Sensitive BuFLO scheme outlined by Cai, et al. CS-BuFLO, which is based on the provably-secure BuFLO defense proposed by Dyer, et al., was not fully-specified by Cai, et al, but has nonetheless attracted the attention of the Tor developers. Our experiments find that CS-BuFLO has high overhead (around 2.3-2.8x) but can get 6x closer to the bandwidth/security trade-off lower bound than Tor or plain SSH.