154 citations · 196 across the 9 of their papers we have counts for
9 papers
Pixel-Optimization-Free Patch Attack on Stereo Depth Estimation
Hangcheng Liu, Xu Kuang, Xingshuo Han +6
Stereo Depth Estimation (SDE) is essential for scene perception in vision-based systems such as autonomous driving. Prior work shows SDE is vulnerable to pixel-optimization attacks…
SwitchPatch: Physical Adversarial Attack Strategy with Switchable Adversarial Objectives
Hanrui Jiang, Yutong Wu, Shiyi Yao +5
Physical adversarial patch (PAP) attacks attach carefully crafted patches to physical objects to manipulate a deployed model. However, existing PAP attacks suffer from several limi…
Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability
Hao Wang, Shangwei Guo, Jialing He +3
Pre-trained models (PTMs) are widely adopted across various downstream tasks in the machine learning supply chain. Adopting untrustworthy PTMs introduces significant security risks…
Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding
Hangcheng Liu, Tao Xiang, Shangwei Guo +3
Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In t…
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang +4
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing th…
Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding
Han Li, Hangcheng Liu, Shangwei Guo +4
Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing w…
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Renhe Jiang, Du Yin, Zhaonan Wang +7
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car…
PEEL: A Provable Removal Attack on Deep Hiding
Tao Xiang, Hangcheng Liu, Shangwei Guo +1
Deep hiding, embedding images into another using deep neural networks, has shown its great power in increasing the message capacity and robustness. In this paper, we conduct an in-…
Local Black-box Adversarial Attacks: A Query Efficient Approach
Tao Xiang, Hangcheng Liu, Shangwei Guo +2
Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting wi…