400 citations · 755 across the 28 of their papers we have counts for
28 papers
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
Shuyu Cheng, Yibo Miao, Yinpeng Dong +3
This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to i…
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
Lingxuan Wu, Xiao Yang, Yinpeng Dong +3
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. However, the prevailing defenses depend on…
BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators
Yu Tian, Xiao Yang, Yinpeng Dong +3
Extremely large image generators offer significant transformative potential across diverse sectors. It allows users to design specific prompts to generate realistic images through…
Your Diffusion Model is Secretly a Certifiably Robust Classifier
Huanran Chen, Yinpeng Dong, Shitong Shao +4
Generative learning, recognized for its effective modeling of data distributions, offers inherent advantages in handling out-of-distribution instances, especially for enhancing rob…
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
Yao Huang, Yinpeng Dong, Shouwei Ruan +3
Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater thr…
Evil Geniuses: Delving into the Safety of LLM-based Agents
Yu Tian, Xiao Yang, Jingyuan Zhang +2
Rapid advancements in large language models (LLMs) have revitalized in LLM-based agents, exhibiting impressive human-like behaviors and cooperative capabilities in various scenario…
On Evaluating Adversarial Robustness of Large Vision-Language Models
Yunqing Zhao, Tianyu Pang, Chao Du +4
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptabl…
A Recipe for Watermarking Diffusion Models
Yunqing Zhao, Tianyu Pang, Chao Du +3
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks. Widespread interest exists in incorporating DMs into downstream applications, such as producing…
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang +2
The trade-off between robustness and accuracy has been widely studied in the adversarial literature. Although still controversial, the prevailing view is that this trade-off is inh…
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Shilong Liu, Feng Li, Hao Zhang +5
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This…
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
Yichun Shi, Xiao Yang, Yangyue Wan +1
Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are design…
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.…
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
Xiao Yang, Yinpeng Dong, Wenzhao Xiang +3
The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness. However, the progress is…
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…
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Xiao Yang, Yinpeng Dong, Tianyu Pang +2
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several methods have demonstrated impressive untargeted transferability, however, it is s…
Accumulative Poisoning Attacks on Real-time Data
Tianyu Pang, Xiao Yang, Yinpeng Dong +2
Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.…
Exploring Memorization in Adversarial Training
Yinpeng Dong, Ke Xu, Xiao Yang +4
Deep learning models have a propensity for fitting the entire training set even with random labels, which requires memorization of every training sample. In this paper, we explore…
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis
Xiaoyu Xiang, Ding Liu, Xiao Yang +3
In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of t…
Black-box Detection of Backdoor Attacks with Limited Information and Data
Yinpeng Dong, Xiao Yang, Zhijie Deng +4
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments. A malicious backdoor could be embedded in a model by…
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Zhijie Deng, Xiao Yang, Shizhen Xu +2
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples. Various adversarial defense strategies have been proposed to resolve t…
Bag of Tricks for Adversarial Training
Tianyu Pang, Xiao Yang, Yinpeng Dong +2
Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are l…
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…