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Keyword

FGSM

Description: The Fast Gradient Sign Method, a gradient-based technique for generating adversarial examples to test or attack machine-learning models.
OpenAlex ID: keywords/fgsm
Improving Transferability of Adversarial Examples With Input Diversity
2019 · Cihang Xie, Zhishuai Zhang, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 1,340
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Fast is better than free: Revisiting adversarial training
2020 · Wong, Eric, Rice, Leslie, et al. · arXiv (Cornell University) · 682
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On the (Statistical) Detection of Adversarial Examples
2017 · Grosse, Kathrin, Manoharan, Praveen, et al. · arXiv (Cornell University) · 546
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A study of the effect of JPG compression on adversarial images
2016 · Dziugaite, Gintare Karolina, Ghahramani, Zoubin, et al. · arXiv (Cornell University) · 388
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Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
2019 · Lin, Jiadong, Song, Chuanbiao, et al. · arXiv (Cornell University) · 376
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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack
2019 · Zhezhi He, Adnan Siraj Rakin, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 276
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
2017 · Das, Nilaksh, Shanbhogue, Madhuri, et al. · arXiv (Cornell University) · 241
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Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
2019 · Bryse Flowers, R. Michael Buehrer, et al. · IEEE Transactions on Information Forensics and Security · 189
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Adversarial Perturbations Fool Deepfake Detectors
2020 · Apurva Gandhi, Shomik Jain · 158
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Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors
2021 · Minh-Quang Tran, Meng-Kun Liu, et al. · IEEE Transactions on Instrumentation and Measurement · 139
Understanding and Improving Fast Adversarial Training
2020 · Andriushchenko, Maksym, Flammarion, Nicolas · arXiv (Cornell University) · 133
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Adversarial Training Methods for Deep Learning: A Systematic Review
2022 · Weimin Zhao, Sanaa Alwidian, et al. · Algorithms · 128
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How Does Mixup Help With Robustness and Generalization?
2020 · Zhang, Linjun, Deng, Zhun, et al. · arXiv (Cornell University) · 116
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On the Vulnerability of CNN Classifiers in EEG-Based BCIs
2019 · Xiao Zhang, Dongrui Wu · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 109
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Reliable IoT Paradigm With Ensemble Machine Learning for Faults Diagnosis of Power Transformers Considering Adversarial Attacks
2023 · Mahmoud N. Ali, Mohammed Amer, et al. · IEEE Transactions on Instrumentation and Measurement · 95
Adversarial Attacks and Defense for CNN Based Power Quality Recognition in Smart Grid
2021 · Jiwei Tian, Buhong Wang, et al. · IEEE Transactions on Network Science and Engineering · 91
Understanding Catastrophic Overfitting in Single-step Adversarial Training
2021 · Hoki Kim, Woojin Lee, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 89
SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking
2020 · Qing Guo, Xiaofei Xie, et al. · Lecture notes in computer science · 85
Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system
2023 · Khushnaseeb Roshan, Aasim Zafar, et al. · Computer Communications · 83
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Improving the Generalization of Adversarial Training with Domain Adaptation
2018 · Song, Chuanbiao, He, Kun, et al. · arXiv (Cornell University) · 83
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Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model
2023 · Xuan-Ha Nguyen, Kim-Hung Le · Internet of Things · 81
Adversarial Noise Layer: Regularize Neural Network by Adding Noise
2019 · Zhonghui You, Jinmian Ye, et al. · Proceedings - International Conference on Image Processing · 80
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Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
2021 · Pavlos Papadopoulos, Oliver Thornewill von Essen, et al. · Journal of Cybersecurity and Privacy · 79
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HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise
2021 · Souvik Kundu, Massoud Pedram, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 76
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Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems
2021 · Jinxin Liu, Michele Nogueira, et al. · IEEE Communications Surveys & Tutorials · 75
Works count: 1,770
Citation count: 13,440
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