116 citations · 444 across the 35 of their papers we have counts for
10 papers · 1 filter
A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection
Fu Wang, Yanghao Zhang, Xiangyu Yin +4
Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and rel…
Towards Fairness-Aware Adversarial Learning
Yanghao Zhang, Tianle Zhang, Ronghui Mu +2
Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e.…
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Yi Zhang, Yun Tang, Wenjie Ruan +4
Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions. However, as is common with many Deep…
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Zheng Wang, Wenjie Ruan
Recent research on the robustness of deep learning has shown that Vision Transformers (ViTs) surpass the Convolutional Neural Networks (CNNs) under some perturbations, e.g., natura…
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking
Xiangyu Yin, Wenjie Ruan, Jonathan Fieldsend
The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain…
3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models
Ronghui Mu, Wenjie Ruan, Leandro S. Marcolino +1
3D point cloud models are widely applied in safety-critical scenes, which delivers an urgent need to obtain more solid proofs to verify the robustness of models. Existing verificat…
Sparse Adversarial Video Attacks with Spatial Transformations
Ronghui Mu, Wenjie Ruan, Leandro Soriano Marcolino +1
In recent years, a significant amount of research efforts concentrated on adversarial attacks on images, while adversarial video attacks have seldom been explored. We propose an ad…
Adversarial Driving: Attacking End-to-End Autonomous Driving
Han Wu, Syed Yunas, Sareh Rowlands +2
As research in deep neural networks advances, deep convolutional networks become promising for autonomous driving tasks. In particular, there is an emerging trend of employing end-…
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks
Yanghao Zhang, Fu Wang, Wenjie Ruan
Although there are a great number of adversarial attacks on deep learning based classifiers, how to attack object detection systems has been rarely studied. In this paper, we propo…
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
Yanghao Zhang, Wenjie Ruan, Fu Wang +1
The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However,…