Towards Evaluating the Robustness of Neural Networks

Cited by (1,019)

Items 801–900 of 1,019

1. Towards Robustness of Deep Program Processing Models—Detection, Estimation, and Enhancement
   Huangzhao Zhang, Zhiyi Fu, Ge Li, Lei Ma, Zhehao Zhao, Hua’an Yang, Yizhe Sun, Yang Liu, Zhi Gang Jin · 2022 · ACM Transactions on Software Engineering and Methodology
2. FGMD: A robust detector against adversarial attacks in the IoT network
   Hongling Jiang, Jinzhi Lin, Haiyan Kang · 2022 · Future Generation Computer Systems
3. Surakav: Generating Realistic Traces for a Strong Website Fingerprinting Defense
   Jiajun Gong, Wuqi Zhang, Charles Zhang, Tao Wang · 2022 · IEEE Symposium on Security and Privacy (S&P)
4. MMA-Diffusion: MultiModal Attack on Diffusion Models
   Yijun Yang, Ruiyuan Gao, Xiaosen Wang, Tsung-Yi Ho, Nan Xu, Qiang Xu · 2024 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
5. Deep neural rejection against adversarial examples
   Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, Fabio Roli · 2020 · EURASIP Journal on Information Security
6. Cascade Adversarial Machine Learning Regularized with a Unified Embedding
   Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay · 2017 · arXiv (Cornell University)
7. Adversarially robust transfer learning
   Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, Tom Goldstein · 2019 · arXiv (Cornell University)
8. Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks
   Yali Du, Meng Fang, Jinfeng Yi, Chang Xu, Jun Sheng Cheng, Dacheng Tao · 2018 · IEEE Transactions on Multimedia
9. Minimal Adversarial Examples for Deep Learning on 3D Point Clouds
   Jaeyeon Kim, Binh‐Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
10. Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
    Athena Sayles, Ashish Hooda, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
11. Adversarial Attacks On Multi-Agent Communication
    James Tu, Tsun-Hsuan Johnson Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
12. GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
    Sungyoon Lee, Hoki Kim, Jaewook Lee · 2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence
13. Adversarial Example Detection Using Latent Neighborhood Graph
    Ahmed Abusnaina, Yuhang Wu, Sunpreet S. Arora, Yizhen Wang, Fei Wang, Hao Yang, Aziz Mohaisen · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
14. Exploring Targeted and Stealthy False Data Injection Attacks via Adversarial Machine Learning
    Jiwei Tian, Buhong Wang, Jing Li, Zhen Wang, Bowen Ma, Mete Özay · 2022 · IEEE Internet of Things Journal
15. A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
    Haomin Zhuang, Yihua Zhang, Sijia Liu · 2023 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
16. Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
    Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, Jiayu Zhou · 2018 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
17. Rallying Adversarial Techniques against Deep Learning for Network Security
    Joseph Clements, Yuzhe Yang, Ankur A. Sharma, Hongxin Hu, Yingjie Lao · 2021 · IEEE Symposium Series on Computational Intelligence (SSCI)
18. A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification
    Cui-Ping Shi, Rui-Yang Xia, Li-Guo Wang · 2020 · IEEE Access
19. Towards Query-Efficient Adversarial Attacks Against Automatic Speech Recognition Systems
    Qian Wang, Baolin Zheng, Qi Lecky Li, Chao Qun Shen, Zhongjie Ba · 2020 · IEEE Transactions on Information Forensics and Security
20. Exploring the Space of Black-box Attacks on Deep Neural Networks
    Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Xiaodong Song · 2017 · arXiv (Cornell University)
21. Robust Local Features for Improving the Generalization of Adversarial Training
    Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, John E. Hopcroft · 2019 · arXiv (Cornell University)
22. The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey
    Olakunle Ibitoye, Rana Abou Khamis, Shehaby, Mohamed el, Ashraf Matrawy, M. Omair Shafiq · 2019 · arXiv (Cornell University)
23. A Survey of Black-Box Adversarial Attacks on Computer Vision Models
    Siddhant Bhambri, Sumanyu Muku, Avinash Tulasi, Arun Balaji Buduru · 2019 · arXiv (Cornell University)
24. On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
    Binghui Wang, Xiaoyu Cao, jia, Jinyuan, Neil Zhenqiang Gong · 2020 · arXiv (Cornell University)
25. Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
    Utku Özbulak, Arnout Van Messem, Wesley De Neve · 2019 · Lecture notes in computer science
26. Single-Step Adversarial Training With Dropout Scheduling
    B S Vivek, R. Venkatesh Babu · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
27. Revisiting Batch Normalization for Improving Corruption Robustness
    Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon · 2021 · IEEE Winter Conference on Applications of Computer Vision
28. Perfectly parallel fairness certification of neural networks
    Caterina Urban, Maria Christakis, Valentin Wüstholz, Fuyuan Zhang · 2020 · Proceedings of the ACM on Programming Languages
29. Learning to Adversarially Blur Visual Object Tracking
    Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
30. TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
    Bao Gia Doan, Minhui Xue, Shiqing Ma, Ehsan Abbasnejad, Damith Chinthana Ranasinghe · 2022 · IEEE Transactions on Information Forensics and Security
31. NPC: N euron P ath C overage via Characterizing Decision Logic of Deep Neural Networks
    Xiaofei Xie, Tianlin Li, Jian Wang, Lei Ma, Qing Guo, Felix Juefei-Xu, Yang Liu · 2022 · ACM Transactions on Software Engineering and Methodology
32. Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack
    Xiaosen Wang, Zeliang Zhang, Kangheng Tong, Dihong Gong, Kun He, Zhifeng Li, Wei Liu · 2022 · Lecture notes in computer science
33. GeCo
    Maximilian Schleich, Zixuan Geng, Yihong Zhang, Dan Mircea Suciu · 2021 · Proceedings of the VLDB Endowment
34. Evading Watermark based Detection of AI-Generated Content
    Zhengyuan Jiang, Jinghuai Zhang, Neil Zhenqiang Gong · 2023 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
35. Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review
    Bakary Badjie, José Cecílio, António Casimiro · 2024 · ACM Computing Surveys
36. To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy to Generate Unsafe Images ... For Now
    Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Sijia Liu, Ke Ding, Sijia Liu · 2024 · Lecture notes in computer science
37. Adversarial Examples Detection in Features Distance Spaces
    Fabio Carrara, Rudy Becarelli, Roberto Caldelli, Fabrizio Falchi, Giuseppe M. Amato · 2019 · Lecture notes in computer science
38. Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?
    Akshay Agarwal, Richa Singh, Mayank Vatsa A, Nalini Ratha · 2018 · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)
39. CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification
    Xuefeng Jiang, Yikun Wang, Wenbo Liu, Shuying Li, Junrui Liu · 2019 · International Journal of Machine Learning and Computing
40. Can Adversarial Weight Perturbations Inject Neural Backdoors
    Siddhant Garg, Adarsh Kumar, Vibhor Goel, Yingyu Liang · 2020 · ACM International Conference on Information and Knowledge Management (CIKM)
41. Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip
    Weilun Chen, Zhaoxiang Zhang, Xiaolin Hu, Baoyuan Wu · 2020 · Lecture notes in computer science
42. Attacking Binarized Neural Networks
    Angus Galloway, Graham W. Taylor, Medhat A. Moussa · 2017 · arXiv (Cornell University)
43. Fooling a Real Car with Adversarial Traffic Signs
    Nir Morgulis, Alexander Kreines, Shachar Mendelowitz, Yuval Weisglass · 2019 · arXiv (Cornell University)
44. Natural Language Adversarial Attacks and Defenses in Word Level
    Xiaosen Wang, Hao Jin, Kun He · 2019 · arXiv (Cornell University)
45. Towards More Practical Adversarial Attacks on Graph Neural Networks
    Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei · 2020 · arXiv (Cornell University)
46. Limitations of the Lipschitz Constant as a Defense Against Adversarial Examples
    Todd P. Huster, Cho‐Yu Jason Chiang, Ritu Chadha · 2019 · Lecture notes in computer science
47. Poltergeist: Acoustic Adversarial Machine Learning against Cameras and Computer Vision
    Xiaoyu Ji, Yushi Cheng, Yuepeng Zhang, Kai Wang, Chen Yan, Wenyuan Xu, Kevin E. Fu · 2021 · IEEE Symposium on Security and Privacy (S&P)
48. Towards a Robust Deep Neural Network against Adversarial Texts: A Survey
    Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, Aoshuang Ye · 2021 · IEEE Transactions on Knowledge and Data Engineering
49. Semantics-Preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection
    Lan Zhang, Peng Liu, Yoon-Ho Choi, Ping Chen · 2022 · IEEE Transactions on Dependable and Secure Computing
50. Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
    Wenzhi Fang, Ziyi Yu, Yu-Ning Jiang, Yuan-Ming Shi, C. Jones, Yong Zhou · 2022 · IEEE Transactions on Signal Processing
51. Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack
    Chi Liu, Huajie Chen, Tianqing Zhu, Jun Zhang, Wanlei Zhou · 2023 · IEEE Transactions on Dependable and Secure Computing
52. Boosting the transferability of adversarial attacks with global momentum initialization
    Jiafeng Wang, Zhaoyu Chen, Kaixun Jiang, Dingkang Yang, Lingyi Hong, Pinxue Guo, Haijing Guo, Wenqiang Zhang · 2024 · Expert Systems with Applications
53. Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems
    Lea Schönherr, Thorsten Eisenhofer, Steffen Zeiler, Thorsten Holz, Dorothea Kolossa · 2020 · Annual Computer Security Applications Conference
54. Attacking Graph Convolutional Networks via Rewiring
    Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang · 2019 · arXiv (Cornell University)
55. Security and Privacy for Artificial Intelligence: Opportunities and Challenges
    Ayodeji Oseni, Nour Moustafa, Helge Janicke, Peng Liu, Zahir Tari, Athanasios V. Vasilakos · 2021 · arXiv (Cornell University)
56. MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
    Sailik Sengupta, Tathagata Chakraborti, Subbarao Kambhampati B · 2019 · Lecture notes in computer science
57. HideNoSeek
    Aurore Fass, Michael Backes A, Ben Stock · 2019 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
58. Multitask Learning Strengthens Adversarial Robustness
    Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick · 2020 · Lecture notes in computer science
59. LiBRe: A Practical Bayesian Approach to Adversarial Detection
    Zhijie Deng, Xiao Hui Yang, Shizhen Xu, Hang Su, Jun Zhu · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
60. Class-Aware Robust Adversarial Training for Object Detection
    Pin-Chun Chen, Bo-Han Kung, Jun-Cheng Chen · 2021 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
61. DL-FHMC: Deep Learning-Based Fine-Grained Hierarchical Learning Approach for Robust Malware Classification
    Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary, Afsah Anwar, Rhongho Jang, Saeed Salem, DaeHun Nyang, Aziz Mohaisen · 2021 · IEEE Transactions on Dependable and Secure Computing
62. A New Adversarial Embedding Method for Enhancing Image Steganography
    Minglin Liu, Weiqi Luo, Peijia Zheng, Jiwu Huang · 2021 · IEEE Transactions on Information Forensics and Security
63. Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications
    Md. Mamunur Rashid, Joarder Kamruzzaman, Mohammad Mehedi Hassan, Tasadduq Imam, Santoso Wibowo, Steven Gordon, Giancarlo Fortino · 2022 · Computers & Security
64. Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks
    Ferhat Özgür Çatak, Murat Kuzlu, Evren Çatak, Ümit Cali, Özgur Güler · 2022 · IEEE Access
65. You see what I want you to see: poisoning vulnerabilities in neural code search
    Wan, Yao, Zhang, Shijie, Zhang, Hongyu, Sui, Yulei, Xu, Guandong, Yao, Dezhong, Jin, Hai, Sun, Lichao · 2022 · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
66. Towards Transferable Targeted Adversarial Examples
    Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, Kui Rent · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
67. Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification
    Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, Gérôme Bovet, Gregorio Martínez Pérez · 2023 · Future Generation Computer Systems
68. Adversarial Detection with Model Interpretation
    Ninghao Liu, Hongxia Yang, Xia Hu · 2018 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
69. SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition
    Akhil Goel, Anirudh K. Singh, Akshay Agarwal, Mayank Vatsa A, Richa Singh · 2018 · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)
70. Hu-Fu: Hardware and Software Collaborative Attack Framework Against Neural Networks
    Wenshuo Li, Jincheng Yu, Xuefei Ning, Pengjun Wang, Qi Wei, Yu Wang, Huazhong Yang · 2018 · IEEE Computer Society Annual Symposium on VLSI
71. DDSA: A Defense Against Adversarial Attacks Using Deep Denoising Sparse Autoencoder
    Yassine Bakhti, Sid Ahmed Fezza, Wassim Hamidouche, Olivier Déforges A · 2019 · IEEE Access
72. Universal Adversarial Perturbations Generative Network For Speaker Recognition
    Jiguo Li, Xinfeng Zhang, Chuanmin Jia, Jizheng Xu, Li Zhang, Yue Wang, Siwei Ma, Wen Gao · 2020 · IEEE International Conference on Multimedia and Expo (ICME)
73. TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack
    Yam Sharon, David Berend, Yang Liu, Asaf Shabtai, Yuval Elovici · 2022 · IEEE Transactions on Information Forensics and Security
74. Consistency Regularization for Adversarial Robustness
    Jihoon Tack, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, Jinwoo Shin · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
75. Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
    Kudzai Sauka, Gun-Yoon Shin, Dong-Wook Kim, Myung-Mook Han · 2022 · Applied Sciences
76. Adversarial attacks and defenses in deep learning for image recognition: A survey
    Jia Wang, Chengyu Wang, Qiuzhen Lin, Chengwen Luo, Chao Wu, Jianqiang Li · 2022 · Neurocomputing
77. Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification
    Faris Almalik, Mohammad K. Yaqub, Karthik Nandakumar · 2022 · Lecture notes in computer science
78. A Search-Based Testing Approach for Deep Reinforcement Learning Agents
    Amirhossein Zolfagharian, Manel Abdellatif, Lionel Briand, Mojtaba Bagherzadeh, S. Ramesh · 2023 · IEEE Transactions on Software Engineering
79. GLFF: Global and Local Feature Fusion for AI-Synthesized Image Detection
    Yan Ju, Shan Jia, Jialing Cai, Haiying Guan, Siwei Lyu · 2023 · IEEE Transactions on Multimedia
80. Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search
    Abhimanyu Dubey, Laurens van der Maaten, İsmet Zeki Yalnız, Yixuan Li, Dhruv Kumar Mahajan · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
81. Self-Robust 3D Point Recognition via Gather-Vector Guidance
    Xiaoyi Dong, Dongdong Chen, Hang Zhou, Gang Hua, Weiming Zhang, Nenghai Yu · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
82. Defending Bit-Flip Attack through DNN Weight Reconstruction
    Jingtao Li, Adnan Siraj Rakin, Yan Xiong, Liangliang Chang, Zhezhi He, Deliang Fan, Chaitali Chakrabarti · 2020 · ACM/IEEE Design Automation Conference (DAC)
83. GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
    Moustafa Alzantot, Yash Sharma, Chakraborty, Supriyo, Huan Zhang, Cho‐Jui Hsieh, Mani B. Srivastava · 2018 · arXiv (Cornell University)
84. MixTrain: Scalable Training of Verifiably Robust Neural Networks
    Shiqi Wang, Chen, Yizheng, Ahmed Abdou, Suman Jana · 2018 · arXiv (Cornell University)
85. Convergence of Adversarial Training in Overparametrized Neural Networks
    Ruiqi Gao, Tianle Cai, Haochuan Li, Liwei Wang, Cho‐Jui Hsieh, Jason D. Lee · 2019 · arXiv (Cornell University)
86. AI-GAN: Attack-Inspired Generation of Adversarial Examples
    Bai, Tao, Zhao, Jun, Zhu, Jinlin, Han, Shoudong, Chen, Jiefeng, Li, Bo, Kot, Alex · 2021 · 2021 IEEE International Conference on Image Processing (ICIP)
87. Adversarial Attacks on Spoofing Countermeasures of Automatic Speaker Verification
    Songxiang Liu, Haibin Wu, Hung-yi Lee, Helen M. L. Meng · 2019 · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
88. Testing and verification of neural-network-based safety-critical control software: A systematic literature review
    Jin Zhang, Jingyue Li · 2020 · Information and Software Technology
89. Enabling Fast and Universal Audio Adversarial Attack Using Generative Model
    Yi Xie, Zhuohang Li, Cong Shi, Jian Liu, Yingying Jennifer Chen, Bo Yuan · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
90. Adversarial attacks on deep-learning-based radar range profile target recognition
    Teng Huang, Yongfeng Chen, Bingjian Yao, Bifen Yang, Xianmin Wang, Ya Li · 2020 · Information Sciences
91. Efficient white-box fairness testing through gradient search
    Lingfeng Zhang, Yueling Zhang, Min Zhang · 2021 · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)
92. Def-IDS: An Ensemble Defense Mechanism Against Adversarial Attacks for Deep Learning-based Network Intrusion Detection
    Jianyu Wang, Jianli Pan, Ismail AlQerm, Yuanni Liu · 2021 · International Conference on Computer Communications and Networks (ICCCN)
93. Query-Efficient Black-Box Adversarial Attacks Guided by a Transfer-Based Prior
    Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Jie Zhu · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
94. Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey
    Hao Tan, Le Wang, Huan Zhang, Junjian Zhang, Muhammad Shafiq, Zhaoquan Gu · 2022 · Electronics
95. Adversarial Attacks and Defenses Toward AI-Assisted UAV Infrastructure Inspection
    Ashok Vardhan Raja, Laurent Njilla, Jiawei Yuan · 2022 · IEEE Internet of Things Journal
96. Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
    Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
97. Radio frequency fingerprinting techniques for device identification: a survey
    Sohail Abbas, Manar Wasif Abu Talib, Qassim Nasir, Sally Idhis, Mariam M. Alaboudi, Ali Wagdy Mohamed · 2023 · International Journal of Information Security
98. Improving Fast Adversarial Training With Prior-Guided Knowledge
    Xiaojun Jia, Yong Zhang, Xingxing Wei, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao · 2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence
99. AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0
    M. Nadeem Ahangar, Zohaib A. Farhat, Aparajithan Sivanathan · 2025 · Sensors
100. On the vulnerability of deep learning to adversarial attacks for camera model identification
     Francesco Marra, Diego Gragnaniello, Luisa Verdoliva · 2018 · Signal Processing Image Communication