32 citations · 52 across the 15 of their papers we have counts for
15 papers
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
Xin-Cheng Wen, Xinchen Wang, Cuiyun Gao +3
Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have…
Adversarial Attacks on ASR Systems: An Overview
Xiao Zhang, Hao Tan, Xuan Huang +3
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment bec…
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…
Improving robustness of language models from a geometry-aware perspective
Bin Zhu, Zhaoquan Gu, Le Wang +2
Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness. However, we observe th…
One model Packs Thousands of Items with Recurrent Conditional Query Learning
Dongda Li, Zhaoquan Gu, Yuexuan Wang +2
Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, b…
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Bin Zhu, Zhaoquan Gu, Le Wang +1
Recent work shows that deep neural networks are vulnerable to adversarial examples. Much work studies adversarial example generation, while very little work focuses on more critica…
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
Keke Tang, Dingruibo Miao, Weilong Peng +5
Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a…
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…
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…
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…
Decision Propagation Networks for Image Classification
Keke Tang, Peng Song, Yuexin Ma +4
High-level (e.g., semantic) features encoded in the latter layers of convolutional neural networks are extensively exploited for image classification, leaving low-level (e.g., colo…
Attending Category Disentangled Global Context for Image Classification
Keke Tang, Guodong Wei, Runnan Chen +3
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures…
Robust Component-based Network Localization with Noisy Range Measurements
Tianyuan Sun, Yongcai Wang, Deying Li +2
Accurate and robust localization is crucial for wireless ad-hoc and sensor networks. Among the localization techniques, component-based methods advance themselves for conquering ne…