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 901–1,000 of 1,005

  1. Recent Advances in Large Margin Learning
    Yiwen Guo, Changshui Zhang · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  2. Semantically Stealthy Adversarial Attacks against Segmentation Models
    Zhenhua Chen, Chuhua Wang, David Crandall · 2022 · IEEE Winter Conference on Applications of Computer Vision
  3. Machine Learning for Multimedia Communications
    Nikolaos Thomos, Thomas Maugey, Laura Toni · 2022 · Sensors
  4. Boosting the transferability of adversarial examples via stochastic serial attack
    Lingguang Hao, Kuangrong Hao, Bing Wei, Xue‐song Tang · 2022 · Neural Networks
  5. On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment
    Qun Song, Rui Tan, Chao Ren, Yan Hui Xu, Yang Lou, Jianping Wang, Hoay Beng Gooi · 2022 · IEEE Transactions on Dependable and Secure Computing
  6. UniAP: Protecting Speech Privacy With Non-Targeted Universal Adversarial Perturbations
    Peng Cheng, Yuexin Wu, Yuan Hong, Zhongjie Ba, Feng Lin, Li Lü, Kui Ren · 2023 · IEEE Transactions on Dependable and Secure Computing
  7. Universal Attack Against Automatic Modulation Classification DNNs Under Frequency and Data Constraints
    Chao Wang, Xianglin Wei, Jianhua Fan, Yongyang Hu, Long Yu, Qing Tian · 2023 · IEEE Internet of Things Journal
  8. Exploring Non-target Knowledge for Improving Ensemble Universal Adversarial Attacks
    Juanjuan Weng, Zhiming Luo, Zhun Zhong, Dazhen Lin, Shaozi Li · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
  9. White-box inference attack: compromising the security of deep learning-based COVID-19 diagnosis systems
    Burhan Ul Haque Sheikh, Aasim Zafar · 2023 · International Journal of Information Technology
  10. Transferability of Adversarial Attacks on Tiny Deep Learning Models for IoT Unmanned Aerial Vehicles
    Shan Zhou, Xianting Huang, Mohammad S. Obaidat, Bander A. Alzahrani, Xuming Han, Saru Kumari, Chien‐Ming Chen · 2023 · IEEE Internet of Things Journal
  11. Identifying and preventing future forms of crimes using situational crime prevention
    Shane D. Johnson · 2024 · Security Journal
  12. Temporal characteristics-based adversarial attacks on time series forecasting
    Ziyu Shen, Yun Li · 2024 · Expert Systems with Applications
  13. Towards Analyzing Semantic Robustness of Deep Neural Networks
    Abdullah Hamdi, Bernard Ghanem · 2020 · Lecture notes in computer science
  14. Universal Rules for Fooling Deep Neural Networks based Text Classification
    Di Li, Danilo Vasconcellos Vargas, Kouichi Sakurai · 2019 · IEEE Congress on Evolutionary Computation (CEC)
  15. Patch Selection Denoiser: An Effective Approach Defending Against One-Pixel Attacks
    Dong Chen, Ruqiao Xu, Bo Han · 2019 · Communications in computer and information science
  16. Improving the affordability of robustness training for DNNs
    Sidharth Gupta, Parijat Dube, Ashish Verma · 2020 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  17. Towards Safety Verification of Direct Perception Neural Networks
    Chih‐Hong Cheng, Chung-Hao Huang, Thomas Brunner, Vahid Hashemi · 2020 · Design, Automation, and Test in Europe
  18. Adversarial Attacks on Deep Learning Models of Computer Vision: A Survey
    Jia Ding, Zhiwu Xu · 2020 · Lecture notes in computer science
  19. Fast-UAP: An algorithm for expediting universal adversarial perturbation generation using the orientations of perturbation vectors
    Jiazhu Dai, Le Shu · 2020 · Neurocomputing
  20. Imperceptible Adversarial Examples by Spatial Chroma-Shift
    Ayberk Aydın, Deniz Sen, Berat Tuna Karli, Oğuz Hanoğlu, Alptekin Temizel · 2021
  21. The mathematics of adversarial attacks in AI – why deep learning is unstable despite the existence of stable neural networks
    Alexander Bastounis, Anders C. Hansen, Verner Vlačić · 2025 · European Journal of Applied Mathematics
  22. Human and action recognition using adaptive energy images
    Onur Can Kurban, Nurullah Çalık, Tülay Yıldırım · 2022 · Pattern Recognition
  23. Rule-based adversarial sample generation for text classification
    Nai Zhou, Nianmin Yao, Jian Kui Zhao, Yanan Zhang · 2022 · Neural Computing and Applications
  24. Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
    Kaidi Jin, Tianwei Zhang, Chao Qun Shen, Yufei Chen, Ming Yu Fan, Chenhao Lin, Ting Liu · 2022 · IEEE Transactions on Dependable and Secure Computing
  25. Detecting Adversarial Perturbations in Multi-Task Perception
    Marvin Klingner, V. Kumar, S. Yogamani, Andreas Bär, T. Fingscheidt · 2022 · IEEE/RJS International Conference on Intelligent RObots and Systems
  26. fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems
    Alberto Marchisio, Giovanni Caramia, Maurizio Martina, Muhammad Shafique · 2022 · International Joint Conference on Neural Networks (IJCNN)
  27. Exploring Recurrent and Feedback CNNs for Multi-Spectral Satellite Image Classification
    Nevrez İmamoğlu, Pascual Martínez-Gómez, Ryuhei Hamaguchi, Ken Sakurada, Ryosuke Nakamura · 2018 · Procedia Computer Science
  28. K-Anonymity inspired adversarial attack and multiple one-class classification defense
    Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas · 2020 · Neural Networks
  29. A survey on practical adversarial examples for malware classifiers
    Daniel Park, Bülent Yener · 2020 · Reversing and Offensive-Oriented Trends Symposium
  30. Universal Adversarial Robustness of Texture and Shape-Biased Models
    Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan, Ben Glocker, Emil C. Lupu · 2021 · Proceedings - International Conference on Image Processing
  31. On the Vulnerability of Capsule Networks to Adversarial Attacks
    Félix Michels, Tobias Uelwer, Eric Upschulte, Stefan Harmeling B · 2019 · arXiv (Cornell University)
  32. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
    Yichao Zhou, Jyun‐Yu Jiang, Kai-Wei Chang, Wei Wang · 2019 · arXiv (Cornell University)
  33. Backdoor Attack with Sample-Specific Triggers.
    Yuezun Li, Yiming Li, Baoyuan Wu, Longkang Li, Ran He, Siwei Lyu · 2020 · arXiv (Cornell University)
  34. A case for new neural network smoothness constraints
    Mihaela Rosca, Théophane Weber, Arthur Gretton, Shakir Mohamed · 2020 · arXiv (Cornell University)
  35. A Real-time Defense against Website Fingerprinting Attacks
    Shawn Shan, Arjun Nitin Bhagoji, Hai-Tao Zheng, Ben Y. Zhao · 2021 · arXiv (Cornell University)
  36. Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning
    Guanlin Liu, Lifeng Lai · 2021 · arXiv (Cornell University)
  37. A meta-survey of adversarial attacks against artificial intelligence algorithms, including diffusion models
    Marek Pawlicki, Aleksandra Pawlicka, Rafał Kozik, Michał Choraś · 2025 · Neurocomputing
  38. A comprehensive survey of deep face verification systems adversarial attacks and defense strategies
    Sohair A. Kilany, Ahmed Mahfouz · 2025 · Scientific Reports
  39. Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
    Won Park, Nan Liu, Qi Alfred Chen, Z. Morley Mao · 2021 · Proceedings - International Conference on Image Processing
  40. Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
    Taha Belkhouja, Janardhan Rao Doppa · 2022 · Journal of Artificial Intelligence Research
  41. Why adversarial reprogramming works, when it fails, and how to tell the difference
    Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia, Zetao Jiang, Ambra Demontis, Maura Pintor, Battista Biggio, Fabio Roli · 2023 · Information Sciences
  42. Audio adversarial detection through classification score on speech recognition systems
    Hyun Kwon, Seung-Hun Nam · 2022 · Computers & Security
  43. Generalizing universal adversarial perturbations for deep neural networks
    Yanghao Zhang, Wenjie Ruan, Fu Wang, Xiaowei Huang · 2023 · Machine Learning
  44. Detecting backdoor in deep neural networks via intentional adversarial perturbations
    Mingfu Xue, Yinghao Wu, Zhiyu Wu, Yushu Zhang, Jian Wang, Weiqiang Liu · 2023 · Information Sciences
  45. Practical Disruption of Image Translation Deepfake Networks
    Nataniel Ruiz, Sarah Adel Bargal, Cihang Xie, Stan Sclaroff · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
  46. Cybersecurity in neural interfaces: Survey and future trends
    Xinyu Jiang, Jiahao Fan, Ziyue Zhu, Zihao Wang, Yao Guo, Xiangyu Liu, Fumin Jia, Chenyun Dai · 2023 · Computers in Biology and Medicine
  47. TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization
    Yiran Liu, Xin Feng, Yunlong Wang, Wu Yang, Di Ming · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
  48. Generation and Countermeasures of adversarial examples on vision: a survey
    Jiangfan Liu, Yishan Li, Yanming Guo, Yu Liu, Jun Tang, Ying Nie · 2024 · Artificial Intelligence Review
  49. Adversarial attacks based on time-series features for traffic detection
    Hongyu Lu, Jiajia Liu, Jimin Peng, Jiazhong Lu · 2024 · Computers & Security
  50. Threat Modeling AI/ML With the Attack Tree
    Seied Veria Hoseini, Jaakko Suutala, Juha Partala, Kimmo Halunen · 2024 · IEEE Access
  51. On the Robustness of Object Detection Models on Aerial Images
    Haochen He, Jian Ding, Bowen Xu, Gui-Song Xia · 2024 · IEEE Transactions on Geoscience and Remote Sensing
  52. Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection
    Vincenzo De Rosa, Fabrizio Guillaro, Giovanni Poggi, Davide Cozzolino, Luisa Verdoliva · 2024 · IEEE International Workshop on Information Forensics and Security
  53. Smooth adversarial examples
    Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg · 2020 · EURASIP Journal on Information Security
  54. RL-VAEGAN: Adversarial defense for reinforcement learning agents via style transfer
    Yueyue Hu, Shi-Liang Sun · 2021 · Knowledge-Based Systems
  55. Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis
    Fengting Li, Xuankai Liu, Xiaoli Zhang, Qi Lecky Li, Kun Sun, Kang Li · 2021 · IEEE Conference on Computer Communications (INFOCOM)
  56. Salient feature extractor for adversarial defense on deep neural networks
    Ruoxi Chen, Jinyin Chen, Haibin Zheng, Qi Xuan, Zhaoyan Ming, Wenrong Jiang, Chen Cui · 2022 · Information Sciences
  57. Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images
    Yuki Matsuo, Kazuhiro Takemoto · 2021 · Applied Sciences
  58. Invisible and Efficient Backdoor Attacks for Compressed Deep Neural Networks
    Huy Phan, Yi Xie, Jian Liu, Yingying Jennifer Chen, Bo Yuan · 2022 · IEEE International Conference on Acoustics Speech and Signal Processing
  59. Quantifying safety risks of deep neural networks
    Peipei Xu, Wenjie Ruan, Xiaowei Huang · 2022 · Complex & Intelligent Systems
  60. ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
    Amira Guesmi, Khaled N. Khasawneh, Nael Abu‐Ghazaleh, Ihsen Alouani · 2022 · International Joint Conference on Neural Networks (IJCNN)
  61. Towards Understanding Adversarial Robustness of Optical Flow Networks
    Simon Schrodi, Tonmoy Saikia, Thomas Brox · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  62. Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing
    Fatemeh Stodt, Jan Stodt, Christoph Reich · 2023 · Applied Sciences
  63. Securing DNN for smart vehicles: an overview of adversarial attacks, defenses, and frameworks
    Suzan Almutairi, Ahmed Barnawi · 2023 · Journal of Engineering and Applied Science
  64. A Review on Security Attacks and Protective Strategies of Machine Learning
    K. Meenakshi, G. Maragatham · 2019 · Lecture notes on data engineering and communications technologies
  65. Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations
    Alex Wong, Mukund Mundhra, Stefano Soatto · 2021 · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))
  66. Should Adversarial Attacks Use Pixel p-Norm?
    Ayon Sen, Xiaojin Zhu, Marshall, Liam, Robert D. Nowak · 2019 · arXiv (Cornell University)
  67. What do AI algorithms actually learn? - On false structures in deep learning
    Laura Thesing, Vegard Antun, Anders C. Hansen · 2019 · arXiv (Cornell University)
  68. Towards Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations
    Batuhan Güler, Alexis Laignelet, Panos Parpas · 2019 · arXiv (Cornell University)
  69. Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection
    Mahmood Sharif, Keane Lucas, Lujo Bauer, Michael K. Reiter, Saurabh Shintre · 2019 · arXiv (Cornell University)
  70. On the Robustness of Face Recognition Algorithms Against Attacks and Bias
    Richa Singh, Akshay Agarwal, Maneet Singh, Shruti Nagpal, Mayank Vatsa A · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
  71. Text Captcha Defense Algorithm Based on Overall Adversarial Perturbations
    Shoujin Wang, Guanghe Zhao, Jifei Liu · 2021 · Journal of Physics Conference Series
  72. Lie to Me: Shield Your Emotions from Prying Software
    Alina Elena Baia, Giulio Biondi, Valentina Franzoni, Alfredo Milani, Valentina Poggioni · 2022 · Sensors
  73. A Meta-Heuristic Optimization Based Less Imperceptible Adversarial Attack on Gait Based Surveillance Systems
    Muazzam Maqsood, Mustansar Ali Ghazanfar, Irfan Mehmood, Eenjun Hwang, Seungmin Rho · 2022 · Journal of Signal Processing Systems
  74. Vector Quantisation for Robust Segmentation
    Ainkaran Santhirasekaram, Avinash Kori, Mathias H. Winkler, Andrea Rockall, Ben Glocker · 2022 · Lecture notes in computer science
  75. Attack is the Best Defense: Towards Preemptive-Protection Person Re-Identification
    Lin Wang, Wanqian Zhang, Dayan Wu, Fei Zhu, Bo Li · 2022 · ACM International Conference on Multimedia (ACM MM)
  76. A robust adversarial attack against speech recognition with UAP
    Ziheng Qin, Xianglong Zhang, Shujun Li · 2022 · High-Confidence Computing
  77. Consistent attack: Universal adversarial perturbation on embodied vision navigation
    Chengyang Ying, You Qiaoben, Xinning Zhou, Hang Su, Wenbo Ding, Jianyong Ai · 2023 · Pattern Recognition Letters
  78. Transfer adversarial attacks across industrial intelligent systems
    Zhenqin Yin, Yue Zhuo, Zhiqiang Ge · 2023 · Reliability Engineering & System Safety
  79. Interpretability for reliable, efficient, and self-cognitive DNNs: From theories to applications
    Xu Kang, Jie Guo, Bin Song, Binghuang Cai, Hongyu Sun, Zhebin Zhang · 2023 · Neurocomputing
  80. TrojanModel: A Practical Trojan Attack against Automatic Speech Recognition Systems
    Wei Zong, Yang-Wai Chow, Willy Susilo, Kien Do, Svetha Venkatesh · 2023 · IEEE Symposium on Security and Privacy (S&P)
  81. RBGAN: Realistic-generation and balanced-utility GAN for face de-identification
    Yaofang Zhang, Yuchun Fang, Yiting Cao, Jiahua Wu · 2023 · Image and Vision Computing
  82. Stealthy Adversarial Attacks on Machine Learning-Based Classifiers of Wireless Signals
    Wenhan Zhang, M. Krunz, Gregory Ditzler · 2024 · IEEE Transactions on Machine Learning in Communications and Networking
  83. Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition
    Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Meng Du, Yuanli Li · 2024 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  84. ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features
    Peng Cheng, Yuwei Wang, Peng Huang, Zhongjie Ba, Xiaodong Lin, Feng Lin, Li Lü, Kui Ren · 2024 · IEEE Symposium on Security and Privacy (S&P)
  85. Hiding Imperceptible Noise in Curvature-Aware Patches for 3D Point Cloud Attack
    Mingyu Yang, Daizong Liu, Keke Tang, Pan Zhou, Lixing Chen, Junyang Chen · 2024 · Lecture notes in computer science
  86. Explanation-Guided Backdoor Attacks Against Model-Agnostic RF Fingerprinting Systems
    Tianya Zhao, Junqing Zhang, Shiwen Mao, Xuyu Wang · 2024 · IEEE Transactions on Mobile Computing
  87. Adversarial Attacks Against Shared Knowledge Interpretation in Semantic Communications
    Van-Tam Hoang, Van-Linh Nguyen, Rong‐Guey Chang, Po‐Ching Lin, Ren‐Hung Hwang, Trung Q. Duong · 2025 · IEEE Transactions on Cognitive Communications and Networking
  88. The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
    Felix Assion, Peter Schlicht, Florens Greßner, Wiebke Gunther, Fabian Hüger, Nico M. Schmidt, Umair Rasheed · 2019 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  89. Gradients Cannot Be Tamed: Behind the Impossible Paradox of Blocking Targeted Adversarial Attacks
    Ziv Katzir, Yuval Elovici · 2020 · IEEE Transactions on Neural Networks and Learning Systems
  90. Data-Free Adversarial Perturbations for Practical Black-Box Attack
    Zhaoxin Huan, Yulong Wang, Xiaolu Zhang, Lin Shang, Chilin Fu, Jun Zhou · 2020 · Lecture notes in computer science
  91. Generating universal adversarial perturbation with ResNet
    Jian Feng Xu, Heng Liu, Dexin Wu, Fucai Zhou, Chong-zhi Gao, Linzhi Jiang · 2020 · Information Sciences
  92. AdvJND: Generating Adversarial Examples with Just Noticeable Difference
    Zifei Zhang, Kai Qiao, Lingyun Jiang, Linyuan Wang, Jian Chen, Bin Yan · 2020 · Lecture notes in computer science
  93. Universal Spectral Adversarial Attacks for Deformable Shapes
    Arianna Rampini, Franco Pestarini, Luca Cosmo, Simone Melzi, Emanuele Rodolà · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  94. DeepCon: Contribution Coverage Testing for Deep Learning Systems
    Zhiyang Zhou, Wensheng Dou, Jie Liu, Chenxin Zhang, Jun Wei, Dan Ye · 2021 · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  95. Secure machine learning against adversarial samples at test time
    Jing Lin, Laurent Njilla, Kaiqi Xiong · 2022 · EURASIP Journal on Information Security
  96. Public-attention-based Adversarial Attack on Traffic Sign Recognition
    Lijun Chi, Mounira Msahli, Gérard Memmi, Han Qiu · 2023 · IEEE Consumer Communications and Networking Conference
  97. Analyzing Deep Learning for Time-Series Data Through Adversarial Lens in Mobile and IoT Applications
    Taha Belkhouja, Janardhan Rao Doppa · 2020 · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  98. Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks
    Ali Mirzaeian, Jana Košecká, Houman Homayoun, Tinoosh Mohsenin, Avesta Sasan · 2021 · International Symposium on Quality Electronic Design (ISQED)
  99. Playing the Game of Universal Adversarial Perturbations
    Julien Pérolat, Mateusz Malinowski, Bilal Piot, Olivier Pietquin · 2018 · arXiv (Cornell University)
  100. Hidden Trigger Backdoor Attacks
    Saha, Aniruddha, Subramanya, Akshayvarun, Pirsiavash, Hamed · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence