187 citations · 380 across the 41 of their papers we have counts for
42 papers
Classification Committee for Active Deep Object Detection
Lei Zhao, Bo Li, Xingxing Wei
In object detection, the cost of labeling is much high because it needs not only to confirm the categories of multiple objects in an image but also to accurately determine the boun…
Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World
Xingxing Wei, Yao Huang, Yitong Sun +1
Physical adversarial attacks have put a severe threat to DNN-based object detectors. To enhance security, a combination of visible and infrared sensors is deployed in various scena…
Defending Adversarial Patches via Joint Region Localizing and Inpainting
Junwen Chen, Xingxing Wei
Deep neural networks are successfully used in various applications, but show their vulnerability to adversarial examples. With the development of adversarial patches, the feasibili…
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While…
Unified Adversarial Patch for Cross-modal Attacks in the Physical World
Xingxing Wei, Yao Huang, Yitong Sun +1
Recently, physical adversarial attacks have been presented to evade DNNs-based object detectors. To ensure the security, many scenarios are simultaneously deployed with visible sen…
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Alt…
Structured Network Pruning by Measuring Filter-wise Interactions
Wenting Tang, Xingxing Wei, Bo Li
Structured network pruning is a practical approach to reduce computation cost directly while retaining the CNNs' generalization performance in real applications. However, identifyi…
Distributional Modeling for Location-Aware Adversarial Patches
Xingxing Wei, Shouwei Ruan, Yinpeng Dong +1
Adversarial patch is one of the important forms of performing adversarial attacks in the physical world. To improve the naturalness and aggressiveness of existing adversarial patch…
Learning to Pan-sharpening with Memories of Spatial Details
Maoxun Yuan, Tianyi Zhao, Bo Li +1
Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multispectral images (MS) to obt…
Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
Maoxun Yuan, Xingxing Wei
Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attenti…
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation
Shiji Zhao, Xizhe Wang, Xingxing Wei
Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performanc…
Boosting Adversarial Transferability with Learnable Patch-wise Masks
Xingxing Wei, Shiji Zhao
Adversarial examples have attracted widespread attention in security-critical applications because of their transferability across different models. Although many methods have been…
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
Caixin Kang, Yinpeng Dong, Zhengyi Wang +4
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch atta…
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters
Xingxing Wei, Shiji Zhao, Bo li
Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enh…
Improving Fast Adversarial Training with Prior-Guided Knowledge
Xiaojun Jia, Yong Zhang, Xingxing Wei +4
Fast adversarial training (FAT) is an efficient method to improve robustness. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduc…
Physically Adversarial Infrared Patches with Learnable Shapes and Locations
Wei Xingxing, Yu Jie, Huang Yao
Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is necessary to evaluate their robustness against adversarial examples in the real…
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
Yinpeng Dong, Caixin Kang, Jinlai Zhang +6
3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to rea…
Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking
Xingxing Wei, Bangzheng Pu, Shiji Zhao +2
The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis. Given the pivota…
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
Wei Xingxing, Wang Songping, Yan Huanqian
Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much…
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
Xingxing Wei, Ying Guo, Jie Yu +1
Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial p…
Visual Adversarial Attacks and Defenses in the Physical World: A Survey
Xingxing Wei, Bangzheng Pu, Shiji Zhao +2
Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they remain vulnerable to adversarial examples. Adversarial attacks in computer visio…
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Yinpeng Dong, Shouwei Ruan, Hang Su +3
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transform…
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Maoxun Yuan, Yinyan Wang, Xingxing Wei
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) imag…
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Xiaojun Jia, Yong Zhang, Xingxing Wei +4
Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e.,the robus…