74 citations · 191 across the 29 of their papers we have counts for
19 papers · 1 filter
EasyRobust: A Comprehensive and Easy-to-use Toolkit for Robust and Generalized Vision
Xiaofeng Mao, Yuefeng Chen, Rong Zhang +3
Deep neural networks (DNNs) has shown great promise in computer vision tasks. However, machine vision achieved by DNNs cannot be as robust as human perception. Adversarial attacks…
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Yao Zhu, Yuefeng Chen, Wei Wang +7
Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive La…
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging
Xiaojun Jia, Yuefeng Chen, Xiaofeng Mao +5
Fast Adversarial Training (FAT) not only improves the model robustness but also reduces the training cost of standard adversarial training. However, fast adversarial training often…
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
Xiaofeng Mao, Yuefeng Chen, Yao Zhu +4
Practical object detection application can lose its effectiveness on image inputs with natural distribution shifts. This problem leads the research community to pay more attention…
ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing
Xiaodan Li, Yuefeng Chen, Yao Zhu +3
Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. Therefore, in this paper, instead of following the trad…
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
Yao Zhu, Yuefeng Chen, Xiaodan Li +4
With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial ima…
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Chang Liu, Yinpeng Dong, Wenzhao Xiang +7
The robustness of deep neural networks is usually lacking under adversarial examples, common corruptions, and distribution shifts, which becomes an important research problem in th…
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios…
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…
Enhance the Visual Representation via Discrete Adversarial Training
Xiaofeng Mao, Yuefeng Chen, Ranjie Duan +6
Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thu…
D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention
Junyu Lin, Xiaofeng Mao, Yuefeng Chen +3
DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing. However, it suffers from problems such as low performance and slow con…
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Qilong Zhang, Xiaodan Li, Yuefeng Chen +4
Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-mode…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Ranjie Duan, Yuefeng Chen, Dantong Niu +3
Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep N…
Towards Robust Vision Transformer
Xiaofeng Mao, Gege Qi, Yuefeng Chen +5
Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most…
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Xiaodan Li, Jinfeng Li, Yuefeng Chen +5
We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query acce…
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Honggu Liu, Xiaodan Li, Wenbo Zhou +5
The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step o…
Adversarial Examples Detection beyond Image Space
Kejiang Chen, Yuefeng Chen, Hang Zhou +4
Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these a…