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Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

CoRR
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes.
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen   +6 more
semanticscholar   +3 more sources

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An LLM can Fool Itself: A Prompt-Based Adversarial Attack

arXiv.org, 2023
The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness.
Xilie Xu, Keyi Kong, Ninghao Liu, Li-zhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli   +6 more
semanticscholar   +1 more source

EVAA—Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles

IEEE Transactions on Geoscience and Remote Sensing, 2023
In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR) plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep neural networks (DNNs) are able to achieve cutting-edge 3-D object detection
Vishnu Chalavadi, Jayesh Khandelwal, C. Krishna Mohan, Cenkeramaddi Linga Reddy   +3 more
semanticscholar   +1 more source

Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges

ACM Computing Surveys, 2023
Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice.
Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, H. Chen   +4 more
semanticscholar   +1 more source

An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability

IEEE International Conference on Computer Vision, 2023
While the transferability property of adversarial examples allows the adversary to perform black-box attacks (i.e., the attacker has no knowledge about the target model), the transfer-based adversarial attacks have gained great attention.
B. Chen, Jia-Li Yin, Shukai Chen, Bo-Hao Chen, Ximeng Liu   +4 more
semanticscholar   +1 more source

Targeted Adversarial Attack Against Deep Cross-Modal Hashing Retrieval

IEEE transactions on circuits and systems for video technology (Print), 2023
Deep cross-modal hashing has achieved excellent retrieval performance with the powerful representation capability of deep neural networks. Regrettably, current methods are inevitably vulnerable to adversarial attacks, especially well-designed subtle ...
Tianshi Wang, Lei Zhu, Zheng Zhang, Huaxiang Zhang, Junwei Han   +4 more
semanticscholar   +1 more source

Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System

Computer Communications, 2023
Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks.
Khushnaseeb Roshan, Aasim Zafar, Sheikh Burhan Ul Haque   +2 more
semanticscholar   +1 more source

Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
It is a challenging task to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried.
Yucheng Shi, Yahong Han, Q. Hu, Yi Yang, Qi Tian   +4 more
semanticscholar   +1 more source

An Approximated Gradient Sign Method Using Differential Evolution for Black-Box Adversarial Attack

IEEE Transactions on Evolutionary Computation, 2022
Recent studies show that deep neural networks are vulnerable to adversarial attacks in the form of subtle perturbations to the input image, which leads the model to output wrong prediction.
C. Li, Handing Wang, Jun Zhang, W. Yao, Tingsong Jiang   +4 more
semanticscholar   +1 more source

Detection Tolerant Black-Box Adversarial Attack Against Automatic Modulation Classification With Deep Learning

IEEE Transactions on Reliability, 2022
Advances in adversarial attack and defense technologies will enhance the reliability of deep learning (DL) systems spirally. Most existing adversarial attack methods make overly ideal assumptions, which creates the illusion that the DL system can be ...
Peihan Qi, Tao Jiang, Lizhang Wang, Xu Yuan, Zan Li   +4 more
semanticscholar   +1 more source