Universal Adversarial Perturbations

Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, Fawzi, Omar, Frossard, Pascal·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Cited by (1,005)

Items 201–300 of 1,005

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    Jie Li, Rongrong Ji, Hong Liu, Xiaopeng Hong, Yue Gao, Qi Chuan Tian · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
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  3. UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging.
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  4. Hidden Backdoors in Human-Centric Language Models
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  5. Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems
    Jiwei Tian, Buhong Wang, Zhen Wang, Kunrui Cao, Jing Li, Mete Ozay · 2021 · IEEE Transactions on Cybernetics
  6. Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey
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  7. GeoDA: A Geometric Framework for Black-Box Adversarial Attacks
    Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard B, 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
  8. Adversarial Threats to DeepFake Detection: A Practical Perspective
    Neekhara, Paarth, Dolhansky, Brian, Bitton, Joanna, Ferrer, Cristian Canton · 2021 · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  9. 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
  10. 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
  11. Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations
    Mandlekar, Ajay, Zhu, Yuke, Garg, Animesh, Fei-Fei, Li, Savarese, Silvio · 2017 · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  12. Adversarial camera stickers: A physical camera-based attack on deep learning systems
    Juncheng Li, Frank R. Schmidt, J. Zico Kolter · 2019 · arXiv (Cornell University)
  13. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
    Yichao Zhou, Jyun‐Yu Jiang, Kai-Wei Chang, Wei William Wang · 2019 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
  14. AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Subsecond Perturbations
    Zhuohang Li, Yi Wu, Jian Liu, Yingying Jennifer Chen, Bo Yuan · 2020 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
  15. The Translucent Patch: A Physical and Universal Attack on Object Detectors
    Alon Zolfi, Moshe Kravchik, Yuval Elovici, Asaf Shabtai · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  16. Adversarial Attack and Defense: A Survey
    Hongshuo Liang, Erlu He, Yangyang Zhao, Zhe Jia, Hao Li · 2022 · Electronics
  17. Untitled work
    2018 · arXiv (Cornell University)
  18. Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning
    Jaekyeom Kim, Hyoungseok Kim, Gunhee Kim · 2020 · Lecture notes in computer science
  19. Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video
    Hemin Ali Qadir, Ilangko Balasingham, Johannes Solhusvik, Jacob Bergsland, Lars Aabakken, Younghak Shin · 2019 · IEEE Journal of Biomedical and Health Informatics
  20. Gradient Shielding: Towards Understanding Vulnerability of Deep Neural Networks
    Zhaoquan Gu, Weixiong Hu, Chuanjing Zhang, Hui Dong Lu, Lihua Yin, Le Wang · 2020 · IEEE Transactions on Network Science and Engineering
  21. Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
    Liao, Cong, Zhong, Haoti, Squicciarini, Anna, Zhu, Sencun, Miller, David · 2018 · arXiv
  22. A game-based approximate verification of deep neural networks with provable guarantees
    Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska · 2019 · Theoretical Computer Science
  23. Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
    Sizhe Chen, Zhengbao He, Chengjin Sun, Jie Yang, Xiaolin Huang · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  24. Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning
    Uyeong Jang, Xi Wu, Somesh Jha · 2017 · Annual Computer Security Applications Conference
  25. Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision
    Damien Teney, Ehsan Abbasnedjad, Anton van den Hengel · 2020 · Lecture notes in computer science
  26. Art of Singular Vectors and Universal Adversarial Perturbations
    Ivan Valer'evich Oseledets, Valentin Khrulkov · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  27. Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets
    Riccardo Volpi, Vittorio Murino · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  28. Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems
    Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy Chowdhury, Ananthram Swami · 2019 · Network and Distributed System Security Symposium
  29. Poisoning and Evasion Attacks Against Deep Learning Algorithms in Autonomous Vehicles
    Wenbo Jiang, Hongwei Li, Sen Liu, Xizhao Luo, Rongxing Lu · 2020 · IEEE Transactions on Vehicular Technology
  30. Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems
    Ka-Ho Chow, Ling Liu, Margaret L. Loper, Juhyun Bae, Mehmet Emre Gürsoy, Stacey Truex, Wenqi Wei, Yanzhao Wu · 2020 · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
  31. Interpretable Deep Learning under Fire
    Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang · 2018 · arXiv (Cornell University)
  32. Adversarial attacks on an oblivious recommender
    Christakopoulou, Konstantina, Banerjee, Arindam · 2019 · Proceedings of the 13th ACM Conference on Recommender Systems
  33. Universal Adversarial Attacks on Text Classifiers
    Melika Behjati, Seyed-Mohsen Moosavi-Dezfooli, Mahdieh Soleymani Baghshah, Pascal Frossard B · 2019 · IEEE International Conference on Acoustics Speech and Signal Processing
  34. Rethinking the Trigger of Backdoor Attack
    Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu‐Tao Xia · 2020 · arXiv (Cornell University)
  35. Towards Security Threats of Deep Learning Systems: A Survey
    Yingzhe He, Guozhu Meng, Kai Chen, Xingbo Hu, Jinwen He · 2020 · IEEE Transactions on Software Engineering
  36. Backdooring Convolutional Neural Networks via Targeted Weight Perturbations
    Jacob Dumford, Walter J. Scheirer · 2020 · IEEE International Joint Conference on Biometrics (IJCB)
  37. Deep networks for system identification: A survey
    Gianluigi Pillonetto, Aleksandr Y. Aravkin, Daniel Gedon, Lennart Ljung A, Antônio H. Ribeiro, Thomas B. Schön · 2024 · Automatica
  38. Adversarial Reprogramming of Neural Networks
    Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl‐Dickstein · 2018 · arXiv (Cornell University)
  39. Interpreting Adversarial Examples in Deep Learning: A Review
    Sicong Han, Chenhao Lin, Chao Qun Shen, Qian Wang, Xiaohong Guan · 2023 · ACM Computing Surveys
  40. When Machine Learning Meets Privacy: A Survey and Outlook
    Bo Liu, Ming Ding, Sina Shaham, Wenny J. Rahayu, Farhad Farokhi, Zihuai Lin · 2020 · arXiv (Cornell University)
  41. Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues
    Doowon Jeong · 2020 · IEEE Access
  42. Attacks and defenses in user authentication systems: A survey
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  43. A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis
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  44. Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification
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  45. Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation
    Liu, Hong, Ji, Rongrong, Li, Jie, Zhang, Baochang, Gao, Yue, Wu, Yongjian, Huang, Feiyue · 2019 · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  46. Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
    Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, Chinmay Hegde · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  47. Physical Adversarial Attacks on an Aerial Imagery Object Detector
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  48. Practical Blind Membership Inference Attack via Differential Comparisons
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  49. Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey
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  50. EVAA—Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles
    Chalavadi Vishnu, Jayesh Khandelwal, Chalavadi Krishna Mohan, Linga Reddy Cenkeramaddi · 2023 · IEEE Transactions on Geoscience and Remote Sensing
  51. An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences
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  52. Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics
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  53. Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples
    Xiaojun Jia, Xingxing Wei, Xiaochun Cao, Xiaoguang Han · 2020 · ACM International Conference on Multimedia (ACM MM)
  54. Adversarial examples: attacks and defenses in the physical world
    Huali Ren, Teng Huang, Hongyang Yan · 2021 · International Journal of Machine Learning and Cybernetics
  55. Adversarial Attacks and Defense for CNN Based Power Quality Recognition in Smart Grid
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  56. A Survey on Universal Adversarial Attack
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  57. Machine learning and blockchain technologies for cybersecurity in connected vehicles
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  58. Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study
    Haifeng Li, Haikuo Huang, Li Chen, Jian Peng, Haozhe Huang, Zhenqi Cui, Xiaoming Mei, Guohua Wu · 2020 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  59. Fooling Thermal Infrared Pedestrian Detectors in Real World Using Small Bulbs
    Xiaopei Zhu, Xiao Li, Jianmin Li, Zheyao Wang, Xiaolin Hu · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
  60. Interference Suppression Using Deep Learning: Current Approaches and Open Challenges
    Taiwo Oyedare, Vijay K. Shah, Daniel J. Jakubisin, Jeffrey H. Reed · 2022 · IEEE Access
  61. Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications
    Guoshun Nan, Zhichun Li, Jinli Zhai, Qimei Cui, Gong Chen, Xin Du, Xuefei Zhang, Xiaofeng Tao, Zhu Han, Tony Q. S. Quek · 2023 · IEEE Journal on Selected Areas in Communications
  62. Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
    Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  63. Heuristic Black-Box Adversarial Attacks on Video Recognition Models
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  64. Adversarial Machine Learning
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  65. Adversarial Examples in Modern Machine Learning: A Review
    Rey Reza Wiyatno, Xu, Anqi, Ousmane Dia, Archy O. de Berker · 2019 · arXiv (Cornell University)
  66. Ask, Acquire, and Attack: Data-Free UAP Generation Using Class Impressions
    Mopuri, Konda Reddy, Uppala, Phani Krishna, Babu, R. Venkatesh · 2018 · Lecture Notes in Computer Science
  67. Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
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  68. Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition.
    Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter · 2018 · arXiv (Cornell University)
  69. Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
    Rob Ashmore, Radu Călinescu, Colin Paterson · 2019 · arXiv (Cornell University)
  70. SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking
    Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu · 2020 · Lecture notes in computer science
  71. Restricted Black-Box Adversarial Attack Against DeepFake Face Swapping
    Junhao Dong, Yuan Wang, Jianhuang Lai, Xiaohua Xie · 2023 · IEEE Transactions on Information Forensics and Security
  72. Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system
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  73. Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
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  74. Deep Dictionary Learning: A PARametric NETwork Approach
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  75. On relating explanations and adversarial examples
    Alexey Ignatiev, Nina Narodytska, João P. Marques-Silva · 2019 · Monash University Research Portal (Monash University)
  76. Adversarial Attacks Against Face Recognition: A Comprehensive Study
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  77. A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
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  78. Fooling Vision and Language Models Despite Localization and Attention Mechanism
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  79. On Generating Transferable Targeted Perturbations
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  80. Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey
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  81. SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
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  82. Robust Adversarial Perturbation on Deep Proposal-based Models
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  83. Reinforcement Learning for Autonomous Defence in Software-Defined Networking
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  84. Physical Adversarial Textures That Fool Visual Object Tracking
    Rey Reza Wiyatno, Anqi Xu · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  85. A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning
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  86. A Survey on Adversarial Attack in the Age of Artificial Intelligence
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  89. DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
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  90. Deep Residual Flow for Out of Distribution Detection
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  92. Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification
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  93. Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
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  94. Adversarial Samples on Android Malware Detection Systems for IoT Systems
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  95. Attacks on state-of-the-art face recognition using attentional adversarial attack generative network
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