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 201–300 of 1,019

  1. Image Super-Resolution as a Defense Against Adversarial Attacks
    Aamir Mustafa, Salman H. Khan, Munawar Hayat, Jianbing Shen, Ling Shao · 2019 · IEEE Transactions on Image Processing
  2. GenAttack
    Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho‐Jui Hsieh, Mani B. Srivastava · 2019 · Genetic and Evolutionary Computation Conference (GECCO)
  3. Defense Against Universal Adversarial Perturbations
    Naveed Akhtar, Jian Liu, Ajmal Saeed Mian · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  4. Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
    Nuno Martins, José Magalhães Cruz, Tiago Cruz, Pedro Henriques Abreu · 2020 · IEEE Access
  5. Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions
    Αναστάσιος Γιάνναρος, Aristeidis Karras, Leonidas Theodorakopoulos, Christos Karras, Panagiotis Kranias, Nikolaos Schizas, Gerasimos Kalogeratos, Dimitrios K. Tsolis · 2023 · Journal of Cybersecurity and Privacy
  6. Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
    Guangke Chen, Sen Chen, Lingling Fan, Xiaoning Du, Zhe Zhao, Fu Song, Yang Liu · 2021 · IEEE Symposium on Security and Privacy (S&P)
  7. Adversarial Examples: Attacks and Defenses for Deep Learning
    Xiaoyong Yuan, Pan Li He, Qile Zhu, Xiaolin Li · 2017 · arXiv (Cornell University)
  8. On the Convergence and Robustness of Adversarial Training
    Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu · 2021 · arXiv (Cornell University)
  9. Frequency Domain Model Augmentation for Adversarial Attack
    Yuyang Long, Qilong Zhang, Boheng Zeng, Lianli Gao, Xianglong Liu, Jian Zhang, Jingkuan Song · 2022 · Lecture notes in computer science
  10. Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment
    Ziqi Yang, Jiyi Zhang, Ee‐Chien Chang, Zhenkai Liang · 2019 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  11. Label-Only Membership Inference Attacks
    Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot · 2020 · arXiv (Cornell University)
  12. Adversarially Robust Distillation
    Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
  13. Improving DNN Robustness to Adversarial Attacks Using Jacobian Regularization
    Daniel Jakubovitz, Raja Giryes · 2018 · Lecture notes in computer science
  14. The Robustness of Deep Networks: A Geometrical Perspective
    Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard B · 2017 · IEEE Signal Processing Magazine
  15. Intelligent financial fraud detection practices in post-pandemic era
    Xiaoqian Zhu, Xiang Ao, Zidi Qin, Yanpeng Chang, Yang Liu, Qing He, Jianping Li · 2021 · The Innovation
  16. Improving Black-box Adversarial Attacks with a Transfer-based Prior
    Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu · 2019 · arXiv (Cornell University)
  17. Adversarial Attacks Against Deep Learning-Based Network Intrusion Detection Systems and Defense Mechanisms
    Chaoyun Zhang, Xavier Costa‐Pérez, Paul Patras · 2022 · IEEE/ACM Transactions on Networking
  18. Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
    Hussain, Shehzeen, Neekhara, Paarth, Jere, Malhar, Koushanfar, Farinaz, McAuley, Julian · 2021 · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
  19. Trustworthy ai: A computational perspective
    Liu, Haochen, Wang, Yiqi, Fan, Wenqi, Liu, Xiaorui, Li, Yaxin, Jain, Shaili, Liu, Yunhao, Jain, Anil, Tang, Jiliang · 2023 · ACM Transactions on Intelligent Systems and Technology
  20. Membership Leakage in Label-Only Exposures
    Zheng Li, Yang Zhang · 2021 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  21. Sparse and Imperceivable Adversarial Attacks
    Francesco Croce, Matthias Hein · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  22. Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors
    Dongqi Han, Zhiliang Wang, Ying Chun Zhong, Wenqi Chen, Jiahai Yang, Shuqiang Lu, Xingang Shi, Yin Yan Xia · 2021 · IEEE Journal on Selected Areas in Communications
  23. Adversarial Transformation Networks: Learning to Generate Adversarial Examples
    Shumeet Baluja, Ian Fischer · 2017 · arXiv (Cornell University)
  24. The security of machine learning in an adversarial setting: A survey
    Xianmin Wang, Jing Li, Xiaohui Kuang, Yu‐an Tan, Jin Li · 2019 · Journal of Parallel and Distributed Computing
  25. Machine Learning Security: Threats, Countermeasures, and Evaluations
    Mingfu Xue, Chengxiang Yuan, Heyi Wu, Yushu Zhang, Weiqiang Liu · 2020 · IEEE Access
  26. CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models
    Sharma, Shubham, Henderson, Jette, Ghosh, Joydeep · 2020 · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
  27. Countering Malicious DeepFakes: Survey, Battleground, and Horizon
    Juefei-Xu, Felix, Wang, Run, Huang, Yihao, Guo, Qing, Ma, Lei, Liu, Yang · 2022 · International Journal of Computer Vision
  28. Did you hear that? Adversarial Examples Against Automatic Speech Recognition
    Moustafa Alzantot, Bharathan Balaji, Mani B. Srivastava · 2018 · arXiv (Cornell University)
  29. Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
    Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing‐Long Han · 2021 · IEEE Transactions on Industrial Informatics
  30. Adversarial Attacks and Defenses for Deep-Learning-Based Unmanned Aerial Vehicles
    Jiwei Tian, Buhong Wang, Rongxiao Guo, Zhen Wang, Kunrui Cao, Xiaodong Wang · 2021 · IEEE Internet of Things Journal
  31. Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
    Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, Heiko Hoffmann · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  32. Smart Home Personal Assistants
    Jide Edu, José M. Such, Guillermo Suárez‐Tangil · 2020 · ACM Computing Surveys
  33. A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system
    Akshat Gaurav, Brij Bhooshan Gupta, Prabin Kumar Panigrahi · 2022 · Enterprise Information Systems
  34. An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
    Yao Deng, Xi Zheng, Tianyi Zhang, Chen Chen, Guannan Lou, Miryung Kim · 2020 · IEEE International Conference on Pervasive Computing and Communications
  35. Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
    Brian Kim, Yalin E. Sagduyu, Kemal Davaslıoğlu, Tugba Erpek, Şennur Ulukuş · 2021 · IEEE Transactions on Wireless Communications
  36. Robust Physical-World Attacks on Machine Learning Models.
    Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, Dawn Xiaodong Song · 2017 · arXiv (Cornell University)
  37. Strong mixed-integer programming formulations for trained neural networks
    Ross Anderson, Joey Huchette, Will Ma, Christian Tjandraatmadja, Juan Pablo Vielma · 2020 · Mathematical Programming
  38. Are Labels Required for Improving Adversarial Robustness?
    Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli · 2019 · arXiv (Cornell University)
  39. A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications
    Sijia Liu, Pin‐Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred O. Hero, Pramod K. Varshney · 2020 · IEEE Signal Processing Magazine
  40. Adversarial example detection for DNN models: a review and experimental comparison
    Ahmed Aldahdooh, Wassim Hamidouche, Sid Ahmed Fezza, Olivier Déforges A · 2022 · Artificial Intelligence Review
  41. Are Adversarial Examples Inevitable?
    Ali Shafahi, Wei‐Chin Huang, Christoph Studer, Soheil Feizi, Tom Goldstein · 2018 · arXiv (Cornell University)
  42. Defending network intrusion detection systems against adversarial evasion attacks
    Marek Pawlicki, Michał Choraś, Rafał Kozik · 2020 · Future Generation Computer Systems
  43. Detecting the Unexpected via Image Resynthesis
    Krzysztof Lis, Krishna Kanth Nakka, Pascal Fua, Mathieu Salzmann · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  44. Feature Space Perturbations Yield More Transferable Adversarial Examples
    Nathan Inkawhich, Wei Wen, Hai Helen Li, Yiran Chen · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  45. Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective
    Gabriel Resende Machado, Eugênio Silva, Ronaldo Ribeiro Goldschmidt · 2021 · ACM Computing Surveys
  46. Adversarial Active Learning for Deep Networks: a Margin Based Approach
    Mélanie Ducoffe, Fŕed́eric Precioso · 2018 · arXiv (Cornell University)
  47. You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
    Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong · 2019 · ePrints Soton (University of Southampton)
  48. DARTS: Deceiving Autonomous Cars with Toxic Signs
    Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal · 2018 · arXiv (Cornell University)
  49. Motivating the Rules of the Game for Adversarial Example Research
    Justin Gilmer, Ryan Prescott Adams, Ian Goodfellow, Andersen, David, George E. Dahl · 2018 · arXiv (Cornell University)
  50. DPatch: An Adversarial Patch Attack on Object Detectors
    Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Helen Li, Yiran Chen · 2018 · arXiv (Cornell University)
  51. DaST: Data-Free Substitute Training for Adversarial Attacks
    Mingyi Zhou, Jing Wu, Yipeng Liu, Shuaicheng Liu, Ce Zhu · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  52. Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
    Daniel Liu, Ronald Yu, Hao Su · 2019 · Proceedings - International Conference on Image Processing
  53. Artificial Intelligence Security: Threats and Countermeasures
    Hu, Yupeng, Kuang, Wenxin, Qin, Zheng, Li, Kenli, Zhang, Jiliang, Gao, Yansong, Li, Wenjia, Li, Keqin · 2023 · ACM Computing Surveys
  54. Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
    Xiaoyu Cao, Neil Zhenqiang Gong · 2017 · Annual Computer Security Applications Conference
  55. Semantic Adversarial Examples
    Hossein Hosseini, Radha Poovendran · 2018 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  56. Model-Reuse Attacks on Deep Learning Systems
    Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, Ting Wang · 2018 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  57. Improving Adversarial Transferability via Neuron Attribution-based Attacks
    Jianping Zhang, Weibin Wu, Jen-tse Huang, Yizhan Huang, Wenxuan Wang, Yuxin Su, Michael Rung-Tsong Lyu · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  58. NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
    Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong · 2019 · arXiv (Cornell University)
  59. Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art
    Siddique Latif, Junaid Qadir, Adnan Qayyum, Muhammad Usama, Muhammad Shahzad Younis · 2020 · IEEE Reviews in Biomedical Engineering
  60. Biological constraints on neural network models of cognitive function
    Friedemann Pulvermüller A, Rosario Tomasello, Malte R. Henningsen‐Schomers, Thomas Wennekers · 2021 · Nature reviews. Neuroscience
  61. Adversarial Attacks Against Medical Deep Learning Systems
    Samuel G. Finlayson, Hyung Won Chung, Isaac S. Kohane, Andrew L. Beam · 2018 · arXiv (Cornell University)
  62. Boosting the Transferability of Adversarial Samples via Attention
    Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael Rung-Tsong Lyu, Yu‐Wing Tai · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  63. Intrusion detection based on Machine Learning techniques in computer networks
    Ayesha Siddiqua Dina, Dakshnamoorthy Manivannan · 2021 · Internet of Things
  64. A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
    Karimi, Amir-Hossein, Barthe, Gilles, Schölkopf, Bernhard, Valera, Isabel · 2023 · ACM Computing Surveys
  65. Evading Deepfake-Image Detectors with White- and Black-Box Attacks
    Carlini, Nicholas, Farid, Hany · 2020 · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  66. Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML
    Rasool, Raihan Ur, Ahmad, Hafiz Farooq, Rafique, Wajid, Qayyum, Adnan, Qadir, Junaid · 2022 · Journal of Network and Computer Applications
  67. DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
    Hang Zhou, Kejiang Chen, Weiming Zhang, Han Fang, Wenbo Zhou, Nenghai Yu · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  68. QEBA: Query-Efficient Boundary-Based Blackbox Attack
    Huichen Li, Xiaojun Xu, Xiaolu Zhang, Shuang Yang, Bo Hu Li · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  69. A general framework for adversarial examples with objectives
    Sharif, Mahmood, Bhagavatula, Sruti, Bauer, Lujo, Reiter, Michael K. · 2019 · ACM Transactions on Privacy and Security
  70. Unravelling Robustness of Deep Learning Based Face Recognition Against Adversarial Attacks
    Gaurav Goswami, Nalini Ratha, Akshay Agarwal, Richa Singh, Mayank Vatsa A · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
  71. Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review
    Jaroslav Vrchota, Martin Pech, Ladislav Rolínek, Jiří Bednář · 2020 · Sustainability
  72. Testing Deep Neural Networks
    Youcheng Sun, Xiaowei Huang, Daniel Kroening, Sharp, James, Hill, Matthew, Ashmore, Rob · 2018 · arXiv (Cornell University)
  73. The Information Bottleneck Problem and its Applications in Machine Learning
    Ziv Goldfeld, Yury Polyanskiy · 2020 · IEEE Journal on Selected Areas in Information Theory
  74. Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance
    Zhengyu Zhao, Zhuoran Liu, Martha Larson · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  75. Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
    Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  76. Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
    Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, Peixin Zhang · 2019 · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
  77. When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks
    Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu, Dahua Lin · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  78. LAS-AT: Adversarial Training with Learnable Attack Strategy
    Xiaojun Jia, Yong Zhang, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  79. Seeing isn't Believing
    Yue Zhao, Hong Guo Zhu, Ruigang Liang, Qintao Shen, Shengzhi Zhang, Kai Chen · 2019 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  80. IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
    Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong · 2021 · ACM Asia Conference on Computer and Communications Security (ASIA CCS)
  81. Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
    Yulong Wang, Tong Sheng Sun, Shenghong Li, Xin Yuan, Wei Ni, Ekram Hossain, H. Vincent Poor · 2023 · IEEE Communications Surveys & Tutorials
  82. Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
    Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Hai-Tao Zheng, Ben Y. Zhao · 2020 · arXiv (Cornell University)
  83. Decentralized federated learning through proxy model sharing
    Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh · 2023 · Nature Communications
  84. Adversarial machine learning :
    Apostol Vassilev, Alina Oprea, Alie Jean Fordyce, Hyrum S. Anderson · 2024
  85. Learning Universal Adversarial Perturbations with Generative Models
    Jamie Hayes, George Danezis · 2018 · IEEE Security and Privacy Workshops (SPW)
  86. APE-GAN: Adversarial Perturbation Elimination with GAN
    Guoqing Jin, Shiwei Shen, Dongming Zhang, Feng Dai, Yongdong Zhang · 2019 · IEEE International Conference on Acoustics Speech and Signal Processing
  87. AdvFaces: Adversarial Face Synthesis
    Debayan Deb, Jianbang Zhang, Anil K. Jain · 2020 · IEEE International Joint Conference on Biometrics (IJCB)
  88. WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
    Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai · 2021 · IEEE Transactions on Image Processing
  89. Towards Stable and Efficient Training of Verifiably Robust Neural Networks
    Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Li, Bo, Duane S. Boning, Cho‐Jui Hsieh · 2019 · arXiv (Cornell University)
  90. Adversarial Machine Learning
    Yevgeniy Vorobeychik, Murat Kantarcıoğlu · 2018 · Synthesis lectures on artificial intelligence and machine learning
  91. Adversarial Perturbations Fool Deepfake Detectors
    Apurva Gandhi, Shomik Jain · 2020 · International Joint Conference on Neural Networks (IJCNN)
  92. Towards Robust Pattern Recognition: A Review
    Xu-Yao Zhang, Cheng‐Lin Liu, Ching Y. Suen · 2020 · Proceedings of the IEEE
  93. Constructing Unrestricted Adversarial Examples with Generative Models
    Yang Song, Rui Shu, Nate Kushman, Stefano Ermon · 2018 · arXiv (Cornell University)
  94. Towards Adversarially Robust Object Detection
    Haichao Zhang, Jianyu Wang · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  95. Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
    Mohamed Amine Ferrag, Othmane Friha, Burak Kantarcı, Norbert Tihanyi, Lucas Carvalho Cordeiro, Merouane Abdelkader Debbah, Djallel Hamouda, Muna Sulieman Al-Hawawreh, Kim‐Kwang Raymond Choo · 2023 · IEEE Communications Surveys & Tutorials
  96. Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review
    Zhenyong Zhang, Mengxiang Liu, Mingyang Sun, Ruilong Deng, Peng Cheng, Dusit Tao Niyato, Mo–Yuen Chow, Jiming Chen · 2024 · IEEE Internet of Things Journal
  97. Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
    Salem, Ahmed, Bhattacharya, Apratim, Backes, Michael, Fritz, Mario, Zhang, Yang · 2020 · Universität des Saarlandes
  98. SemanticAdv: Generating Adversarial Examples via Attribute-Conditioned Image Editing
    Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, Bo Hu Li · 2020 · Lecture notes in computer science
  99. Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity
    Shuai Zhou, Chi Liu, Dayong Ye, Tianqing Zhu, Wanlei Zhou, Philip S. Yu · 2022 · ACM Computing Surveys
  100. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
    Naveed Akhtar, Ajmal Saeed Mian · 2018 · arXiv (Cornell University)