114 citations · 125 across the 13 of their papers we have counts for
13 papers
Robust Backdoor Attacks on Object Detection in Real World
Yaguan Qian, Boyuan Ji, Shuke He +4
Deep learning models are widely deployed in many applications, such as object detection in various security fields. However, these models are vulnerable to backdoor attacks. Most b…
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Xiaoyu Liang, Yaguan Qian, Jianchang Huang +4
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep lea…
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Yaguan Qian, Yuqi Wang, Bin Wang +3
Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has…
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing
Wujie Zhou, Shaohua Dong, Caie Xu +1
RGB thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extracti…
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Xiang Ling, Lingfei Wu, Jiangyu Zhang +9
Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evol…
Versailles-FP dataset: Wall Detection in Ancient
Wassim Swaileh, Dimitrios Kotzinos, Suman Ghosh +3
Access to historical monuments' floor plans over a time period is necessary to understand the architectural evolution and history. Such knowledge bases also helps to rebuild the hi…
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…
Person Re-identification based on Robust Features in Open-world
Yaguan Qian, Anlin Sun
Deep learning technology promotes the rapid development of person re-identifica-tion (re-ID). However, some challenges are still existing in the open-world. First, the existing re-…
Visually Imperceptible Adversarial Patch Attacks on Digital Images
Yaguan Qian, Jiamin Wang, Bin Wang +4
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. Ho…
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
Yaguan Qian, Qiqi Shao, Jiamin Wang +5
With the boom of edge intelligence, its vulnerability to adversarial attacks becomes an urgent problem. The so-called adversarial example can fool a deep learning model on the edge…
TEAM: We Need More Powerful Adversarial Examples for DNNs
Yaguan Qian, Ximin Zhang, Bin Wang +4
Although deep neural networks (DNNs) have achieved success in many application fields, it is still vulnerable to imperceptible adversarial examples that can lead to misclassificati…
TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples
Ya-guan Qian, Xi-Ming Zhang, Wassim Swaileh +5
Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effect…
Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
Ya-guan Qian, Dan-feng Ma, Bin Wang +5
Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work…