Trojaning Attack on Neural Networks

Cited by (1,002)

Items 101–200 of 1,002

1. Untitled work
   2018 · arXiv (Cornell University)
2. BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning
   Zhenting Wang, Juan Zhai, Shiqing Ma · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
3. Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
   Liao, Cong, Zhong, Haoti, Squicciarini, Anna, Zhu, Sencun, Miller, David · 2018 · arXiv
4. DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips
   Fan Yao, Adnan Siraj Rakin, Deliang Fan · 2020 · arXiv (Cornell University)
5. Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications
   Junbao Chen, Jingfeng Xue, Yong Wang, Lu Huang, Thar Baker, Zhixiong Zhou · 2022 · Expert Systems with Applications
6. An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era
   Muhammad Shafique, Theocharis Theocharides, Christos-Savvas Bouganis, Muhammad Abdullah Hanif, Faiq Khalid, Rehan Hafiz, Semeen Rehman · 2018 · Design, Automation, and Test in Europe
7. An Invisible Black-Box Backdoor Attack Through Frequency Domain
   Tong Wang, Yuan Yao, Feng Xu, Shengwei An, Hanghang Tong, Ting Wang · 2022 · Lecture notes in computer science
8. Rethinking the Trigger of Backdoor Attack
   Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu‐Tao Xia · 2020 · arXiv (Cornell University)
9. Trojaning Language Models for Fun and Profit
   Xinyang Zhang, Zheng Zhang, Shouling Ji, Ting Wang · 2021 · IEEE European Symposium on Security and Privacy
10. 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
11. Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
    Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, Ruotian Ma, Xipeng Qiu · 2021 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
12. MetaPoison: Practical General-purpose Clean-label Data Poisoning
    Wei‐Chin Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein · 2020 · arXiv (Cornell University)
13. RoFL: Robustness of Secure Federated Learning
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14. When Machine Learning Meets Privacy: A Survey and Outlook
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15. Towards Adversarial Malware Detection
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17. The Threat of Offensive AI to Organizations
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18. Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
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19. Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification
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20. Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
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21. Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
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22. Backdoor Defense with Machine Unlearning
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23. Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
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24. Backdoor Attacks on Self-Supervised Learning
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25. Membership Inference Attacks Against Adversarially Robust Deep Learning Models
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26. Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
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27. Sensitive-Sample Fingerprinting of Deep Neural Networks
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28. Machine learning and blockchain technologies for cybersecurity in connected vehicles
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29. On the Robustness of Backdoor-based Watermarking in Deep Neural Networks
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30. Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
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31. T-BFA: T argeted B it- F lip Adversarial Weight A ttack
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32. An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences
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33. WAFFLE: Watermarking in Federated Learning
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34. Causality-Based Neural Network Repair
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35. Black-box Detection of Backdoor Attacks with Limited Information and Data
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36. Model Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning
    Ayub, Md. Ahsan, Johnson, William A., Talbert, Douglas A., Siraj, Ambareen · 2020 · 2020 54th Annual Conference on Information Sciences and Systems (CISS)
37. A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
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38. Data-Free Backdoor Removal Based on Channel Lipschitzness
    Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu · 2022 · Lecture notes in computer science
39. Model Agnostic Defence Against Backdoor Attacks in Machine Learning
    Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, Sudipta Chattopadhyay · 2022 · IEEE Transactions on Reliability
40. DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints
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41. You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
    Roei Schuster, Congzheng Song, Eran Tromer, Vitaly Shmatikov · 2020 · arXiv (Cornell University)
42. Rethinking Stealthiness of Backdoor Attack against NLP Models
    Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, Xu Sun · 2021
43. Detecting Poisoning Attacks on Machine Learning in IoT Environments
    Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Amir Safavi, Rui Zhang · 2018 · IEEE International Congress on Internet of Things (ICIOT)
44. Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems
    Hadi Abdullah, Muhammad Sajidur Rahman, Washington García, Kevin Warren, Anurag Swarnim Yadav, Tom Shrimpton, Patrick Traynor · 2021 · IEEE Symposium on Security and Privacy (S&P)
45. Neural network laundering: Removing black-box backdoor watermarks from deep neural networks
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46. Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability
    Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna · 2021 · IEEE European Symposium on Security and Privacy
47. Correlations between deep neural network model coverage criteria and model quality
    Shenao Yan, Guanhong Tao, Xuwei Liu, Juan Zhai, Shiqing Ma, Lei Xu, Xiangyu Zhang · 2020 · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)
48. Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
    Avi Schwarzschild, Micah Goldblum, Arjun K. Gupta, John P. Dickerson, Tom Goldstein · 2020 · arXiv (Cornell University)
49. Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
    Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski · 2019 · IEEE Transactions on Visualization and Computer Graphics
50. Stealthy Backdoor Attack for Code Models
    Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo · 2024 · IEEE Transactions on Software Engineering
51. An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
    Jiang, Wenxin, Synovic, Nicholas, Hyatt, Matt, Schorlemmer, Taylor R., Sethi, Rohan, Lu, Yung-Hsiang, Thiruvathukal, George K., Davis, James C. · 2023 · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
52. SentiNet: Detecting Physical Attacks Against Deep Learning Systems
    Edward Chou, Florian Tramèr, Giancarlo Pellegrino, Dan Boneh · 2018 · arXiv (Cornell University)
53. Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
    Erwin Quiring, Konrad Rieck · 2020 · IEEE Security and Privacy Workshops (SPW)
54. Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems
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55. TrojDRL: Evaluation of Backdoor Attacks on Deep Reinforcement Learning
    Panagiota Kiourti, Kacper Wardega, Susmit Jha, Wenchao Li · 2020 · ACM/IEEE Design Automation Conference (DAC)
56. Hardware Trojan Attacks on Neural Networks
    Joseph Clements, Yingjie Lao · 2018 · arXiv (Cornell University)
57. Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers
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58. DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
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59. BlockFLA
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60. Gotta Catch'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
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61. A Systematic Review of Adversarial Machine Learning Attacks, Defensive Controls and Technologies
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62. The "Beatrix" Resurrections: Robust Backdoor Detection via Gram Matrices
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63. Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects
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64. A Survey on Neural Trojans
    Yuntao Liu, Ankit Mondal, Abhishek Chakraborty, Michael Zuzak, Nina Jacobsen, Daniel Xing, Ankur Srivastava · 2020 · International Symposium on Quality Electronic Design (ISQED)
65. ProFlip: Targeted Trojan Attack with Progressive Bit Flips
    Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
66. A survey on cybersecurity attacks and defenses for unmanned aerial systems
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67. Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient
    Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie · 2021 · IEEE Transactions on Neural Networks and Learning Systems
68. Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
    Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, Xiangyu Zhang · 2021 · arXiv (Cornell University)
69. Towards Inspecting and Eliminating Trojan Backdoors in Deep Neural Networks
    Wenbo Guo, Lun Wang, Yan Hui Xu, Xinyu Xing, Min Du, Dawn Xiaodong Song · 2020 · Proceedings
70. Adversarial Unlearning of Backdoors via Implicit Hypergradient
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71. RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
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72. NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations
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73. Truth Serum
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74. The Internet of Federated Things (IoFT)
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75. One-to-N & N-to-One: Two Advanced Backdoor Attacks Against Deep Learning Models
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76. TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets
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77. What Do You See?: Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors
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78. On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
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79. SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
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80. TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
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81. Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges [Review Article]
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82. Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
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83. VLA
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84. GangSweep
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85. DAWN: Dynamic Adversarial Watermarking of Neural Networks
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86. Backdoor Attack on Machine Learning Based Android Malware Detectors
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87. Better Trigger Inversion Optimization in Backdoor Scanning
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88. Multi-metrics adaptively identifies backdoors in Federated learning
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89. Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies
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90. ADFL: Defending backdoor attacks in federated learning via adversarial distillation
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91. Enhancing Fine-Tuning based Backdoor Defense with Sharpness-Aware Minimization
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92. ModelDiff: testing-based DNN similarity comparison for model reuse detection
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93. Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
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94. NNrepair: Constraint-Based Repair of Neural Network Classifiers
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95. Piccolo: Exposing Complex Backdoors in NLP Transformer Models
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96. Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-level Backdoor Attacks
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97. SAFELearning: Secure Aggregation in Federated Learning With Backdoor Detectability
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98. Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy.
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99. Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving
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100. TrojViT: Trojan Insertion in Vision Transformers
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