224 citations · 3.2k across the 170 of their papers we have counts for
36 papers · 1 filter
How Robust is Google's Bard to Adversarial Image Attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen +6
Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However,…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
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…
Confidence-based Reliable Learning under Dual Noises
Peng Cui, Yang Yue, Zhijie Deng +1
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet,…
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.…
Adversarial Semantic Contour for Object Detection
Yichi Zhang, Zijian Zhu, Xiao Yang +1
Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by …
Query2Label: A Simple Transformer Way to Multi-Label Classification
Shilong Liu, Lei Zhang, Xiao Yang +2
This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence o…
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models
Zihao Xiao, Xianfeng Gao, Chilin Fu +5
Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security se…
Unsupervised Part Segmentation through Disentangling Appearance and Shape
Shilong Liu, Lei Zhang, Xiao Yang +2
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure a…
Rethinking Natural Adversarial Examples for Classification Models
Xiao Li, Jianmin Li, Ting Dai +3
Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples"…
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Qijun Luo, Zhili Liu, Lanqing Hong +7
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasin…
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
Chen Ma, Shuyu Cheng, Li Chen +2
We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance in the score-based setting. SWITCH features a hig…
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng +5
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is…
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Xiao Yang, Dingcheng Yang, Yinpeng Dong +3
Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks. However, it has raised security concerns in enormous FR applications…
Benchmarking Adversarial Robustness
Yinpeng Dong, Qi-An Fu, Xiao Yang +4
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts h…
Design and Interpretation of Universal Adversarial Patches in Face Detection
Xiao Yang, Fangyun Wei, Hongyang Zhang +1
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors. Unlike previous wor…
Countering Noisy Labels By Learning From Auxiliary Clean Labels
Tsung Wei Tsai, Chongxuan Li, Jun Zhu
We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we al…
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
Yinpeng Dong, Hang Su, Baoyuan Wu +4
Face recognition has obtained remarkable progress in recent years due to the great improvement of deep convolutional neural networks (CNNs). However, deep CNNs are vulnerable to ad…
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Yinpeng Dong, Tianyu Pang, Hang Su +1
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is…
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Zhijie Deng, Yucen Luo, Jun Zhu
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain wit…
Transferable Adversarial Attacks for Image and Video Object Detection
Xingxing Wei, Siyuan Liang, Ning Chen +1
Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection. However, the existing attacking methods for object detection have two…
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio +20
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop…
Sparse Adversarial Perturbations for Videos
Xingxing Wei, Jun Zhu, Hang Su
Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, att…