Towards Evaluating the Robustness of Neural Networks

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Items 401–500 of 1,019

1. Universal Perturbation Attack Against Image Retrieval
   Jie Li, Rongrong Ji, Hong Liu, Xiaopeng Hong, Yue Gao, Qi Chuan Tian · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
2. Generating Adversarial Examples for Holding Robustness of Source Code Processing Models
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3. Untrained Neural Network Priors for Inverse Imaging Problems: A Survey
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4. Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art
   Xiang Ling, Lingfei Wu, Jiangyu Zhang, Zhenqing Qu, Wei Deng, Xiang Chen, Yaguan Qian, Chunming Wu, Shouling Ji, Tianyue Luo et al. · 2023 · Computers & Security
5. Data Security Issues in Deep Learning: Attacks, Countermeasures, and Opportunities
   Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert Huijie Deng · 2019 · IEEE Communications Magazine
6. GeoDA: A Geometric Framework for Black-Box Adversarial Attacks
   Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Huaiyu Dai · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
7. Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
   Francesco Croce, Maksym Andriushchenko, Naman Deep Singh, Nicolas Flammarion, Matthias Hein · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
8. Scaling provable adversarial defenses
   Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter · 2018 · arXiv (Cornell University)
9. EC2: Ensemble Clustering and Classification for Predicting Android Malware Families
   Tanmoy Chakraborty, Fabio Pierazzi, V. S. Subrahmanian · 2017 · IEEE Transactions on Dependable and Secure Computing
10. Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
    Saehyung Lee, Hyungyu Lee, Sungroh Yoon · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
11. Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
    Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin‐Yu Chen, Jinjun Xiong, Meng Wang · 2020 · Lecture notes in computer science
12. Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks
    Jianyu Wang, Haichao Zhang · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
13. Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks
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14. Stateful Detection of Black-Box Adversarial Attacks
    Steven Chen, Nicholas Carlini, David Wagner · 2020 · Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligence
15. Perturbation-Seeking Generative Adversarial Networks: A Defense Framework for Remote Sensing Image Scene Classification
    Gong Cheng, Xuxiang Sun, Ke Li, Lei Guo, Junwei Han · 2021 · IEEE Transactions on Geoscience and Remote Sensing
16. Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling
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17. Adversarial camera stickers: A physical camera-based attack on deep learning systems
    Juncheng Li, Frank R. Schmidt, J. Zico Kolter · 2019 · arXiv (Cornell University)
18. Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
    Minhao Cheng, Simranjit Singh, Patrick M. Chen, Pin‐Yu Chen, Sijia Liu, Cho‐Jui Hsieh · 2019 · arXiv (Cornell University)
19. Torchattacks: A PyTorch Repository for Adversarial Attacks
    Hoki Kim · 2020 · arXiv (Cornell University)
20. Towards Proving the Adversarial Robustness of Deep Neural Networks
    Katz, Guy, Barrett, Clark, Dill, David L., Julian, Kyle, Kochenderfer, Mykel J. · 2017 · Electronic Proceedings in Theoretical Computer Science
21. AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Subsecond Perturbations
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22. Training Robust Deep Neural Networks via Adversarial Noise Propagation
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23. AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
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24. On the Effectiveness of Low Frequency Perturbations
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25. Backdoor Attacks Against Transfer Learning With Pre-Trained Deep Learning Models
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26. Physical Adversarial Attack Meets Computer Vision: A Decade Survey
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27. Unrestricted Adversarial Examples via Semantic Manipulation
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28. Adversarial Defense via Learning to Generate Diverse Attacks
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29. Gradient-based Adversarial Attacks against Text Transformers
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30. The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems
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31. Adversarial Attack and Defense: A Survey
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32. Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
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33. Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning
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34. Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
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35. Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
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36. Adversarial Threats to DeepFake Detection: A Practical Perspective
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37. Needle in a Haystack
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38. Maximizing information from chemical engineering data sets: Applications to machine learning
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39. The Robust Manifold Defense: Adversarial Training using Generative Models
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40. Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
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41. Improving Robustness using Generated Data
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42. Gradient Shielding: Towards Understanding Vulnerability of Deep Neural Networks
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43. Realistic Adversarial Data Augmentation for MR Image Segmentation
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44. SurFree: a fast surrogate-free black-box attack
    Thibault Maho, Teddy Furon, Erwan Le Merrer · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
45. Test Selection for Deep Learning Systems
    Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, Yves Le Traon · 2021 · ACM Transactions on Software Engineering and Methodology
46. Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
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47. Functional Adversarial Attacks
    Cassidy Laidlaw, Soheil Feizi · 2019 · arXiv (Cornell University)
48. Fixing Data Augmentation to Improve Adversarial Robustness
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49. Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks
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50. A game-based approximate verification of deep neural networks with provable guarantees
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51. On the Vulnerability of CNN Classifiers in EEG-Based BCIs
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52. Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
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53. Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework
    Hailin Feng, Qing Li, Wei William Wang, Ali Kashif Bashir, Amit Kumar Singh, Jinshan Xu, Kai Fang · 2024 · Information Fusion
54. Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness
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55. Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning
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56. Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better
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57. MANDA: On Adversarial Example Detection for Network Intrusion Detection System
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58. Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
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59. Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
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60. Structural Test Coverage Criteria for Deep Neural Networks
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61. DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips
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62. One-Class Classification: A Survey
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63. Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity
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64. LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks
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65. Repairing deep neural networks
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66. TDGIA: Effective Injection Attacks on Graph Neural Networks
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67. Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
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68. Interpretable Deep Learning under Fire
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69. Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
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70. With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning
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71. Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
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72. Detecting botnet by using particle swarm optimization algorithm based on voting system
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73. Daedalus: Breaking Nonmaximum Suppression in Object Detection via Adversarial Examples
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74. Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems
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75. Rethinking the Trigger of Backdoor Attack
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76. Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
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77. Phantom of the ADAS: Securing Advanced Driver-Assistance Systems from Split-Second Phantom Attacks
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78. Trojaning Language Models for Fun and Profit
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81. A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
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89. Branch and Bound for Piecewise Linear Neural Network Verification
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95. Google's Cloud Vision API is Not Robust to Noise
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96. Adversarial Attacks on Face Detectors Using Neural Net Based Constrained Optimization
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97. Robust Adversarial Attacks Detection Based on Explainable Deep Reinforcement Learning for UAV Guidance and Planning
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99. TSViz: Demystification of Deep Learning Models for Time-Series Analysis
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100. Formal Specification for Deep Neural Networks
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