Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time

Xinfeng Li, Chen Yan, Xuancun Lu, Zihan Zeng, Xiaoyu Ji, Wenyuan Xu

Project

• We propose VRIFLE, an inaudible adversarial perturbation (IAP) attack via ultrasound delivery that can manipulate ASRs as a user speaks.
• Github https://github.com/LetterLiGo/Adversarial_Audio_Attack-VRifle

Unity is Strength? Benchmarking the Robustness of Fusion-based 3D Object Detection against Physical Sensor Attack

Zizhi Jin, Xuancun Lu, Bo Yang,Yushi Chen, Chen Yan, Xiaoyu Ji, Wenyuan Xu

Project

• Our new benchmark features 5 types of LiDAR attacks and 6 types of camera attacks. Different from traditional benchmarks, we take the physical sensor attacks into consideration during the corruption construction. Then, we systematically investigate 7 MSF-based and 5 single-modality 3D object detection models with different fusion architectures.
• Homepage https://zjushine.github.io/PSA-Fusion/
• Github https://github.com/Jinzizhisir/PSA-Fusion/

PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

Zizhi Jin, Qinhong Jiang, Xuancun Lu, Chen Yan, Xiaoyu Ji, Wenyuan Xu

Project

• In this paper, we investigate the possibility of cross-modality signal injection attacks, i.e., injecting intentional electromagnetic interference (IEMI) to manipulate LiDAR output. Our insight is that the internal modules of a LiDAR, i.e., the laser receiving circuit, the monitoring sensors, and the beam-steering modules, even with strict electromagnetic compatibility (EMC) testing, can still couple with the IEMI attack signals and result in the malfunction of LiDAR systems. Based on the above attack surfaces, we propose the PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off. We evaluate and demonstrate the effectiveness of PhantomLiDAR with both simulated and real-world experiments on five COTS LiDAR systems. We also conduct feasibility experiments in real-world moving scenarios. We provide potential defense measures that can be implemented at both the sensor level and the vehicle system level to mitigate the risks associated with IEMI attacks.
• Homepage https://sites.google.com/view/phantomlidar