5 papers
Characterizing Trust Boundary Vulnerabilities in TEE Containers: An Empirical Study
Weijie Liu, Hongbo Chen, Shuo Huai +7
Trusted Execution Environments (TEEs) have become a cornerstone of confidential computing, attracting significant attention from academia and industry. To support secure and scalab…
Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
Kangqiao Zhao, Shuo Huai, Xurui Song +1
Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target…
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
Xurui Song, Shuo Huai, JingJing Jiang +2
Vision-Language Model (VLM) driving agents promise explainable end-to-end autonomy by first producing natural-language reasoning and then predicting trajectory planning. However, w…
Dagger Behind Smile: Fool LLMs with a Happy Ending Story
Xurui Song, Zhixin Xie, Shuo Huai +2
The wide adoption of Large Language Models (LLMs) has attracted significant attention from attacks, where adversarial prompts crafted through optimization or m…
Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images
Jiayi Kong, Xurui Song, Shuo Huai +3
While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high-resolution multi-view images, posing notable privacy issues. Indivi…