10 citations · 27 across the 14 of their papers we have counts for
15 papers
VideoPure: Diffusion-based Adversarial Purification for Video Recognition
Kaixun Jiang, Zhaoyu Chen, Jiyuan Fu +3
Recent work indicates that video recognition models are vulnerable to adversarial examples, posing a serious security risk to downstream applications. However, current research has…
Boosting Adversarial Transferability with Spatial Adversarial Alignment
Zhaoyu Chen, Haijing Guo, Kaixun Jiang +6
Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of a…
Improving Adversarial Transferability with Neighbourhood Gradient Information
Haijing Guo, Jiafeng Wang, Zhaoyu Chen +5
Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack scenarios, a considerable attac…
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Yuzheng Wang, Dingkang Yang, Zhaoyu Chen +5
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Exist…
Delving into Decision-based Black-box Attacks on Semantic Segmentation
Zhaoyu Chen, Zhengyang Shan, Jingwen Chang +4
Semantic segmentation is a fundamental visual task that finds extensive deployment in applications with security-sensitive considerations. Nonetheless, recent work illustrates the…
Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks
Zhaoyu Chen, Bo Li, Kaixun Jiang +3
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and id…
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation
Siao Liu, Zhaoyu Chen, Yang Liu +8
Learning a policy with great generalization to unseen environments remains challenging but critical in visual reinforcement learning. Despite the success of augmentation combinatio…
Query-Efficient Decision-based Black-Box Patch Attack
Zhaoyu Chen, Bo Li, Shuang Wu +2
Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce…
Sampling to Distill: Knowledge Transfer from Open-World Data
Yuzheng Wang, Zhaoyu Chen, Jie Zhang +7
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data…
Content-based Unrestricted Adversarial Attack
Zhaoyu Chen, Bo Li, Shuang Wu +3
Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photoreal…
Efficient Decision-based Black-box Patch Attacks on Video Recognition
Kaixun Jiang, Zhaoyu Chen, Hao Huang +5
Although Deep Neural Networks (DNNs) have demonstrated excellent performance, they are vulnerable to adversarial patches that introduce perceptible and localized perturbations to t…
ColoristaNet for Photorealistic Video Style Transfer
Xiaowen Qiu, Ruize Xu, Boan He +3
Photorealistic style transfer aims to transfer the artistic style of an image onto an input image or video while keeping photorealism. In this paper, we think it's the summary stat…
RankDNN: Learning to Rank for Few-shot Learning
Qianyu Guo, Hongtong Gong, Xujun Wei +4
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classifica…
Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization
Jiafeng Wang, Zhaoyu Chen, Kaixun Jiang +5
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously, adversarial…
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Yangji He, Weihan Liang, Dongyang Zhao +4
This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers…