Towards Evaluating the Robustness of Neural Networks

Carlini, Nicholas, Wagner, David·2017·2017 IEEE Symposium on Security and Privacy (SP)

Cited by (1,019)

Items 601–700 of 1,019

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  3. Towards Demystifying Membership Inference Attacks
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  4. Black-Box Adversarial Attack with Transferable Model-based Embedding
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  5. Importance-driven deep learning system testing
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  7. Adversarial Attacks Against Face Recognition: A Comprehensive Study
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  8. Explainable deep learning in healthcare: A methodological survey from an attribution view
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  9. A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
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  10. Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system
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  11. Fooling Vision and Language Models Despite Localization and Attention Mechanism
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  12. Hardware Trojan Design on Neural Networks
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  13. Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
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  14. Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey
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  15. “Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice
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  16. Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model
    Xuan-Ha Nguyen, Kim-Hung Le · 2023 · Internet of Things
  17. Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
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  18. Subpopulation Data Poisoning Attacks
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  19. Reinforcement Learning for Autonomous Defence in Software-Defined Networking
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  20. Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems
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  21. Physical Adversarial Textures That Fool Visual Object Tracking
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  24. A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning
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  25. Robust feature learning for adversarial defense via hierarchical feature alignment
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  26. Adversarial Attack on Large Scale Graph
    Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng · 2021 · IEEE Transactions on Knowledge and Data Engineering
  27. A Survey on Adversarial Attack in the Age of Artificial Intelligence
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  28. Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
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  29. Gradient Leakage Attack Resilient Deep Learning
    Wenqi Wei, Ling Liu · 2021 · IEEE Transactions on Information Forensics and Security
  30. Defending Against Physically Realizable Attacks on Image Classification
    Tong Wu, Liang Tong, Yevgeniy Vorobeychik · 2019 · arXiv (Cornell University)
  31. Robust Superpixel-Guided Attentional Adversarial Attack
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  32. Black-box Adversarial Attacks in Autonomous Vehicle Technology
    K. Naveen Kumar, C. Vishnu, Reshmi Mitra, Chalavadi Krishna Mohan · 2020 · IEEE Applied Imagery Pattern Recognition Workshop
  33. EvadeDroid: A practical evasion attack on machine learning for black-box Android malware detection
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  36. Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective
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  37. DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
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    Ali Dabouei, Sobhan Soleymani, Jeremy Michael Dawson, Nasser M. Nasrabadi · 2019 · IEEE Winter Conference on Applications of Computer Vision
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    Yue Zhao, Yuwei Wu, Caihua Chen, Andrew E. B. Lim · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
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  42. Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective
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  52. Attacks on state-of-the-art face recognition using attentional adversarial attack generative network
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  53. Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
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    Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni, Mirco Marchetti · 2020 · IEEE Transactions on Emerging Topics in Computational Intelligence
  60. AFA: Adversarial fingerprinting authentication for deep neural networks
    Jingjing Zhao, Qingyue Hu, Gaoyang Liu, Xiaoqiang Ma, Fei Chen, Mohammad Mehedi Hassan · 2019 · Computer Communications
  61. Machine Learning Approaches in Cyber Security Analytics
    Tony Thomas, Athira P. Vijayaraghavan, Sabu Emmanuel · 2019 · Springer eBooks
  62. Strong Mixed-Integer Programming Formulations for Trained Neural Networks
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  63. A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
    Sicong Zhang, Xiaoyao Xie, Yang Xu · 2020 · IEEE Access
  64. A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
    Hassan Khazane, Mohammed Ridouani, Fatima Salahdine, Naima Kaabouch · 2024 · Future Internet
  65. Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
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  66. Accurate, reliable and fast robustness evaluation
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  67. Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
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  68. Connecting the Digital and Physical World: Improving the Robustness of Adversarial Attacks
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  69. A Robust Approach for Securing Audio Classification Against Adversarial Attacks
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  70. DiffChaser: Detecting Disagreements for Deep Neural Networks
    Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li · 2019 · International Joint Conference on Artificial Intelligence
  71. CAT: Customized Adversarial Training for Improved Robustness
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  72. Towards characterizing adversarial defects of deep learning software from the lens of uncertainty
    Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun · 2020 · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
  73. Adv-Bot: Realistic adversarial botnet attacks against network intrusion detection systems
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  74. PLA-LiDAR: Physical Laser Attacks against LiDAR-based 3D Object Detection in Autonomous Vehicle
    Zizhi Jin, Xiaoyu Ji, Yushi Cheng, Bo Yang, Chen Yan, Wenyuan Xu · 2023 · IEEE Symposium on Security and Privacy (S&P)
  75. Evading DeepFake Detectors via Adversarial Statistical Consistency
    Hou, Yang, Guo, Qing, Huang, Yihao, Xie, Xiaofei, Ma, Lei, Zhao, Jianjun · 2023 · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  76. A simple framework to enhance the adversarial robustness of deep learning-based intrusion detection system
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  77. Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems
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  78. A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
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  79. Analyzing the Robustness of Open-World Machine Learning
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  80. Scalable attack on graph data by injecting vicious nodes
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  81. A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks
    Shangshang Yang, Ye Tian, Cheng He, Xingyi Zhang, Kay Chen Tan, Yaochu Jin · 2021 · IEEE Transactions on Neural Networks and Learning Systems
  82. Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
    Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre · 2021 · ACM Asia Conference on Computer and Communications Security (ASIA CCS)
  83. Optical Adversarial Attack
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  84. The Role of Explainability in Assuring Safety of Machine Learning in Healthcare
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  85. Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems
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  86. A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
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  87. Fake Node Attacks on Graph Convolutional Networks
    Xiaoyun Wang, Minhao Cheng, Joe Eaton, Cho‐Jui Hsieh, S. Felix Wu · 2022 · Journal of Computational and Cognitive Engineering
  88. Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
    Erwin Quiring, Konrad Rieck · 2020 · IEEE Security and Privacy Workshops (SPW)
  89. Sensitivity Analysis of Deep Neural Networks
    Hai Shu, Hongtu Zhu B · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
  90. A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
    Shaohui Mei, Jiawei Lian, Xiaofei Wang, Yuru Su, Mingyang Ma, Lap‐Pui Chau · 2024 · Journal of Remote Sensing
  91. Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples
    Yixiang Wang, Jiqiang Liu, Jelena Mišić, Vojislav B. Mišić, Shaohua Lv, Xiaolin Chang · 2019 · IEEE Access
  92. Deep Neural Network Ensembles Against Deception: Ensemble Diversity, Accuracy and Robustness
    Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret L. Loper, Emre Gürsoy, Stacey Truex, Yanzhao Wu · 2019 · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)
  93. TrojDRL: Evaluation of Backdoor Attacks on Deep Reinforcement Learning
    Panagiota Kiourti, Kacper Wardega, Susmit Jha, Wenchao Li · 2020 · ACM/IEEE Design Automation Conference (DAC)
  94. Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations.
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  95. Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense
    Bowen Peng, Bo Peng, Jie Zhou, Jianyue Xie, Li Liu · 2022 · IEEE Transactions on Geoscience and Remote Sensing
  96. Infrared Invisible Clothing: Hiding from Infrared Detectors at Multiple Angles in Real World
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  97. Open Sesame! Universal Black-Box Jailbreaking of Large Language Models
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  98. Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
    Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, Hao Chen · 2024 · ACM Computing Surveys
  99. Hardware Trojan Attacks on Neural Networks
    Joseph Clements, Yingjie Lao · 2018 · arXiv (Cornell University)
  100. On Robustness of Neural Ordinary Differential Equations
    Hanshu Yan, Jiawei Du, Vincent Y. F. Tan, Jiashi Feng · 2019 · arXiv (Cornell University)