Trojaning Attack on Neural Networks

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

FaceNet: A unified embedding for face recognition and clustering

Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831

Towards Evaluating the Robustness of Neural Networks

Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

“Librispeech: an ASR corpus based on public domain audio books,”

Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072

Deep Face Recognition

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754

Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445

Versatile Weight Attack via Flipping Limited Bits

Bai, J., Wu, B., Li, Z., & Xia, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3296408 · Cited by 21

Rethinking FPGA Security in the New Era of Artificial Intelligence

Xu, X., & Zhang, J. (2020) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed48828.2020.9136974 · Cited by 17

Exposing Backdoors in Robust Machine Learning Models.Preprint

Soremekun, E., Udeshi, S., Chattopadhyay, S., & Zeller, A. (2020) · arXiv (Cornell University) · Cited by 18

Evaluation of Inference Attack Models for Deep Learning on Medical DataPreprint

Wu, M., Zhang, X., Ding, J., Nguyen, H. V., Rong, Y., Pan, M., & Wong, S. T. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00177 · Cited by 33

Adversarial Patch Attacks and Defences in Vision-Based Tasks: A SurveyPreprint

Sharma, A., Bian, Y., Munz, P., & Narayan, A. (2022) · doi:10.36227/techrxiv.20085902.v1 · Cited by 28

Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-Modal Fake News Detection

Chen, J., Jia, C., Zheng, H., Chen, R., & Fu, C. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3249290 · Cited by 17

Compromising LLM Driven Embodied Agents With Contextual Backdoor Attacks

Liu, A., Zhou, Y., Liu, X., Zhang, T., Liang, S., Wang, J., Pu, Y., Li, T., Zhang, J., Zhou, W., et al. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3555410 · Cited by 28

Luminance-based video backdoor attack against anti-spoofing rebroadcast detection

Bhalerao, A. H., Kallas, K., Tondi, B., & Barni, M. (2019) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2019.8901711 · Cited by 17

Adversarial Vulnerability in Doppler-based Human Activity Recognition

Yang, Z., Zhao, Y. S., & Yan, W. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207686 · Cited by 17

SoK: Anti-Facial Recognition Technology

Wenger, E., Shan, S., Zheng, H.-T., & Zhao, B. Y. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179445 · Cited by 17

AI-Guardian: Defeating Adversarial Attacks using Backdoors

Zhu, H. G., Zhang, S., & Chen, K. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179473 · Cited by 18

A Novel Framework for Smart Cyber Defence: A Deep-Dive Into Deep Learning Attacks and Defences

Arshad, I., Alsamhi, S. H., Qiao, Y., Lee, B., & Ye, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3306333 · Cited by 21

Invisible Backdoor Attacks Using Data Poisoning in Frequency Domain

Yue, C., Lv, P., Liang, R., & Chen, K. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230610 · Cited by 17

QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks

Chu, C., Chen, F., Richerme, P., & Jiang, L. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00124 · Cited by 21

Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data

Abad, G., Ersoy, O., Picek, S., & Urbieta, A. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24334 · Cited by 23

A Privacy-Preserving Aggregation Scheme With Continuous Authentication for Federated Learning in VANETs

Feng, X., Wang, X., Liu, H., Yang, H., & Wang, L. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3369942 · Cited by 18

Programmable Neural Network Trojan for Pre-Trained Feature ExtractorPreprint

Ji, Y., Liu, Z., Hu, X., Wang, P., & Zhang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07766 · Cited by 18

PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields.Preprint

Xiang, C., Bhagoji, A. N., Sehwag, V., & Mittal, P. (2020) · arXiv (Cornell University) · Cited by 18

FaceHack: Triggering backdoored facial recognition systems using facial characteristicsPreprint

Sarkar, E., Benkraouda, H., & Maniatakos, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11623 · Cited by 17

BlockFLA: Accountable Federated Learning via Hybrid Blockchain ArchitecturePreprint

Desai, H. B., Özdayi, M. S., & Kantarcıoğlu, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.07427 · Cited by 17

TROJANZOO: Everything you ever wanted to know about neural backdoors (but were afraid to ask).Preprint

Pang, R., Zhang, Z., Gao, X., Xi, Z., Ji, S., Cheng, P., & Wang, T. (2020) · arXiv (Cornell University) · Cited by 19

One-Shot Backdoor Removal for Federated Learning

Pan, Z., Ying, Z., Wang, Y., Zhang, C., Li, C., & Zhu, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3438150 · Cited by 16

Backdoor smoothing: Demystifying backdoor attacks on deep neural networks

Grosse, K., Lee, T., Biggio, B., Park, Y. H., Michael Backes, A., & Molloy, I. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102814 · Cited by 19

Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning

Tian, Y., Zhang, W., Simpson, A. C., Liu, Y., & Jiang, Z. L. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab192 · Cited by 16

Critical Path-Based Backdoor Detection for Deep Neural Networks

Jiang, W., Wen, X., Zhan, J., Wang, X., Song, Z., & Bian, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3201586 · Cited by 16

Towards Backdoor Attacks and Defense in Robust Machine Learning Models

Soremekun, E., Udeshi, S., & Chattopadhyay, S. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103101 · Cited by 25

BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense

Cheng, S., Tao, G., Liu, Y., An, S., Xu, X., Feng, S., Shen, G., Zhang, K., Xu, Q., Ma, S., et al. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.24944 · Cited by 23

Compression-resistant backdoor attack against deep neural networks

Xue, M., Wang, X., Sun, S., Zhang, Y., Wang, J., & Liu, W. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04575-8 · Cited by 17

DRL-Based Secure Aggregation and Resource Orchestration in MEC-Enabled Hierarchical Federated Learning

Zhao, T., Li, F., & He, L. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3277553 · Cited by 16

Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models

Zhang, Z., Lyu, L., Ma, X., Wang, C., & Sun, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.26 · Cited by 22

Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation

Zhang, Z., Su, Q., & Sun, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.25 · Cited by 17

Practical and General Backdoor Attacks Against Vertical Federated Learning

Xuan, Y., Chen, X., Zhao, Z., Tang, B., & Dong, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43415-0_24 · Cited by 17

Progressive Poisoned Data Isolation for Training-Time Backdoor Defense

Chen, Y., Wu, H., & Zhou, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29023 · Cited by 18

GhostEncoder: Stealthy backdoor attacks with dynamic triggers to pre-trained encoders in self-supervised learning

Wang, Q., Yin, C., Fang, L., Liu, Z., Wang, R., & Lin, C. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103855 · Cited by 16

Review and Critical Analysis of Privacy-Preserving Infection Tracking and Contact Tracing

Buchanan, W. J., Imran, M. A., Rehman, M. U., Zhang, L., Abbasi, Q. H., Chrysoulas, C., Haynes, D., Pitropakis, N., & Papadopoulos, P. (2020) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2020.583376 · Cited by 16

SecureTrain: An Approximation-Free and Computationally Efficient Framework for Privacy-Preserved Neural Network Training

Zhang, Q., Xin, C., & Wu, H. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3040704 · Cited by 16

Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?

Jin, K., Zhang, T., Shen, C. Q., Chen, Y., Fan, M. Y., Lin, C., & Liu, T. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3194642 · Cited by 25

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

Chen, T., Zhang, Z., Zhang, Y., Chang, S., Liu, S., & Wang, Z. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00068 · Cited by 18

A Stealthy Hardware Trojan Exploiting the Architectural Vulnerability of Deep Learning Architectures: Input Interception Attack (IIA)Preprint

Odetola, T. A., Mohammed, H., & Hasan, S. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00783 · Cited by 16

Scratch that! An Evolution-based Adversarial Attack against Neural NetworksPreprint

Jere, M., Rossi, L., Hitaj, B., Ciocarlie, G. F., Boracchi, G., & Koushanfar, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02316 · Cited by 27

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical ChallengesPreprint

Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2020) · arXiv.org · doi:10.48550/arxiv.2012.09110 · Cited by 20

PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models

Zou, W., Geng, R., Wang, B., & Jia, J. (2024) · arXiv · doi:10.48550/arxiv.2402.07867 · Cited by 16

Adversarial Policy Training against Deep Reinforcement Learning

Wu, X., Guo, W., Wei, H., & Xing, X. (2021) · USENIX Security Symposium · Cited by 15

An Imperceptible Data Augmentation Based Blackbox Clean-Label Backdoor Attack on Deep Neural Networks

Xu, C., Liu, W., Zheng, Y., Wang, S., & Chang, C.-H. (2023) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2023.3298802 · Cited by 16

One-to-Multiple Clean-Label Image Camouflage (OmClic) based backdoor attack on deep learning

Wang, G., Ma, H., Gao, Y., Abuadbba, A., Zhang, Z., Kang, W., Al-Sarawi, S. F., Zhang, G., & Abbott, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111456 · Cited by 16

TextGuard: Provable Defense against Backdoor Attacks on Text Classification

Pei, H., Jia, J., Guo, W., Li, B., & Song, D. X. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24090 · Cited by 25

Reverse Backdoor Distillation: Towards Online Backdoor Attack Detection for Deep Neural Network Models

Yao, Z., Zhang, H., Guo, Y., Tian, X., Peng, W., Zou, Y., Zhang, L. Y., & Chen, C. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3369751 · Cited by 16

Poison-Resilient Anomaly Detection: Mitigating Poisoning Attacks in Semi-Supervised Encrypted Traffic Anomaly Detection

Wu, Z., Li, H., Qian, Y., Hua, Y., & Gan, H. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3397719 · Cited by 15

BadCodePrompt: backdoor attacks against prompt engineering of large language models for code generation

Qu, Y., Huang, S., Li, Y., Bai, T., Chen, X., Wang, X., Li, L., & Yao, Y. (2025) · Automated Software Engineering · doi:10.1007/s10515-024-00485-2 · Cited by 19

Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data

Mosafi, I., David, E. O., & Netanyahu, N. S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851798 · Cited by 17

Backdoor Attacks on the DNN Interpretation System

Fang, S., & Choromanska, A. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19935 · Cited by 16

Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

Li, F., Liu, X., Zhang, X., Li, Q. L., Sun, K., & Li, K. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488754 · Cited by 16

FeSHI: Feature Map-Based Stealthy Hardware Intrinsic Attack

Odetola, T. A., Khalid, F., Mohammed, H., Sandefur, T., & Hasan, S. R. (2021) · IEEE Access · doi:10.1109/access.2021.3104520 · Cited by 15

Invisible and Efficient Backdoor Attacks for Compressed Deep Neural Networks

Phan, H., Xie, Y., Liu, J., Chen, Y. J., & Yuan, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747582 · Cited by 15

Design of AI Trojans for Evading Machine Learning-based Detection of Hardware Trojans

Pan, Z., & Mishra, P. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774654 · Cited by 16

Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks

Chen, S., Yang, W., Zhang, Z., Bi, X., & Sun, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.47 · Cited by 17

Trembling triggers: exploring the sensitivity of backdoors in DNN-based face recognition

Pasquini, C., & Böhme, R. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00104-z · Cited by 15

AID: Attesting the Integrity of Deep Neural Networks

Aramoon, O., Chen, P., & Qu, G. (2021) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18074.2021.9586290 · Cited by 14

zkMLaaS: a Verifiable Scheme for Machine Learning as a Service

Huang, C., Wang, J., Chen, H., Si, S., Huang, Z., & Xiao, J. Z. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000784 · Cited by 17

Deepfake Detection Algorithm Based on Dual-Branch Data Augmentation and Modified Attention Mechanism

Wan, D., Cai, M., Peng, S., Qin, W., & Li, L. (2023) · Applied Sciences · doi:10.3390/app13148313 · Cited by 17

TrojanModel: A Practical Trojan Attack against Automatic Speech Recognition Systems

Zong, W., Chow, Y.-W., Susilo, W., Do, K., & Venkatesh, S. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179331 · Cited by 14

SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning

Li, P., Huang, J., Wu, H., Zhang, Z., & Qi, C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106199 · Cited by 16

NeuralSanitizer: Detecting Backdoors in Neural Networks

Zhu, H., Zhao, Y., Zhang, S., & Chen, K. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3390599 · Cited by 14

Mitigating Backdoor Attacks in Pre-Trained Encoders via Self-Supervised Knowledge Distillation

Bie, R., Jiang, J., Xie, H., Guo, Y., Miao, Y., & Jia, X. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3417279 · Cited by 17

Precision strike: Precise backdoor attack with dynamic trigger

Li, Q., Chen, W., Xu, X., Zhang, Y., & Wu, L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104101 · Cited by 14

Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability

Wang, H., Guo, S., He, J., Liu, H., Zhang, T., & Xiang, T. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714624 · Cited by 29

CLEAR: Clean-up Sample-Targeted Backdoor in Neural Networks

Zhu, L., Ning, R., Xin, C., Wang, C., & Wu, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01614 · Cited by 15

A collaborative deep learning microservice for backdoor defenses in Industrial IoT networks

Liu, Q., Chen, L., Jiang, H., Wu, J., Wang, T., Peng, T., & Wang, G. (2021) · Ad Hoc Networks · doi:10.1016/j.adhoc.2021.102727 · Cited by 16

MLCHECK– Property-Driven Testing of Machine Learning Classifiers

Sharma, A., Demir, C., Ngomo, A.-C. N., & Wehrheim, H. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00123 · Cited by 20

Backdoor Attacks on Crowd Counting

Sun, Y., Zhang, T., Ma, X., Zhou, P., Lou, J., Xu, Z., Di, X., Cheng, Y., & Sun, L. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548296 · Cited by 15

On the Feasibility of Training-time Trojan Attacks through Hardware-based Faults in Memory

Cai, K., Zhang, Z., & Yao, F. (2022) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/host54066.2022.9840266 · Cited by 14

Bias Busters: Robustifying DL-Based Lithographic Hotspot Detectors Against Backdooring Attacks

Liu, K., Tan, B., Reddy, G. R., Garg, S., Makris, Y., & Karri, R. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3033749 · Cited by 15

Red Alarm for Pre-trained Models: Universal Vulnerabilities by Neuron-Level Backdoor Attacks.Preprint

Zhang, Z., Xiao, G., Li, Y., Lv, T. S., Qi, F., Liu, Z., Wang, Y., Jiang, X., & Sun, M. (2021) · arXiv (Cornell University) · Cited by 14

How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean DataPreprint

Zhang, Z., Lyu, L., Wang, W., Sun, L., & Sun, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01300 · Cited by 19

Data Poisoning Attack by Label Flipping on SplitFed Learning

Gajbhiye, S., Singh, P., & Gupta, S. (2023) · Communications in Computer and Information Science · doi:10.1007/978-3-031-23599-3_30 · Cited by 13

Malicious AI Models Undermine Software Supply-Chain Security

Sood, A. K., & Zeadally, S. (2025) · Communications of the ACM · doi:10.1145/3704724 · Cited by 13

PriRoAgg: Achieving Robust Model Aggregation With Minimum Privacy Leakage for Federated Learning

Hou, S., Li, S., Jahani-Nezhad, T., & Caire, G. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3577498 · Cited by 14

Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study

Joe, B., Park, Y.-H., Hamm, J., Shin, I., & Lee, J. (2022) · JMIR Medical Informatics · doi:10.2196/38440 · Cited by 14

Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection

Fu, H., Krishnamurthy, P., Garg, S., & Khorrami, F. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3297056 · Cited by 13

LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors

Wei, C., Meng, W., Zhang, Z., Chen, M., Zhao, M., Fang, W., Wang, L., Zhang, Z., & Chen, W. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23238 · Cited by 20

Detection of backdoor attacks using targeted universal adversarial perturbations for deep neural networks

Qu, Y., Huang, S., Chen, X., Wang, X., & Yao, Y. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111859 · Cited by 14

Shadow backdoor attack: Multi-intensity backdoor attack against federated learning

Ren, Q., Zheng, Y., Chao, Y., Li, Y., & Ma, J. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103740 · Cited by 14

A Closer Look at Robustness of Vision Transformers to Backdoor Attacks

Subramanya, A., Koohpayegani, S. A., Saha, A., Tejankar, A., & Pirsiavash, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00383 · Cited by 14

Speech emotion recognition systems and their security aspects

Gurowiec, I., & Nissim, N. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10760-z · Cited by 13

Have You Poisoned My Data? Defending Neural Networks Against Data Poisoning

Gaspari, F. D., Hitaj, D., & Mancini, L. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_5 · Cited by 16

Mitigating backdoor attacks in Federated Learning based intrusion detection systems through Neuron Synaptic Weight Adjustment

Zukaib, U., & Cui, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113167 · Cited by 13

Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency

Kiani, S., Awan, S., Lan, C., Li, F., & Luo, B. (2021) · Annual Computer Security Applications Conference · doi:10.1145/3485832.3485904 · Cited by 15

A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation

Fu, H., Veldanda, A. K., Krishnamurthy, P., Garg, S., & Khorrami, F. (2022) · IEEE Access · doi:10.1109/access.2022.3141077 · Cited by 14

Hibernated Backdoor: A Mutual Information Empowered Backdoor Attack to Deep Neural Networks

Ning, R., Li, J., Xin, C., Wu, H., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21272 · Cited by 14

Imperceptible and multi-channel backdoor attack

Xue, M., Ni, S., Wu, Y., Zhang, Y., & Liu, W. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05228-6 · Cited by 18

Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware

Erichson, N. B., Taylor, D., Wu, Q., & Mahoney, M. W. (2021) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976700.12 · Cited by 18

SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

Odetola, T. A., & Hasan, S. R. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401166 · Cited by 13

L-Red: Efficient Post-Training Detection of Imperceptible Backdoor Attacks Without Access to the Training Set

Xiang, Z. J., Miller, D. J., & Kesidis, G. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414562 · Cited by 13

How to Manipulate CNNs to Make Them Lie: the GradCAM CasePreprint

Viering, T. J., Wang, Z., Loog, M., & Eisemann, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10901 · Cited by 13

Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural NetworksPreprint

Chan, A., & Ong, Y.-S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08040 · Cited by 28

Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic TriggerPreprint

Qi, F., Li, M., Chen, Y., Zhang, Z., Liu, Z., Wang, Y., & Sun, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.12400 · Cited by 13

SoK: Machine Learning GovernancePreprint

Chandrasekaran, V., Jia, H., Thudi, A., Travers, A., Yaghini, M., & Papernot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10870 · Cited by 33

Adversarial Patch Attacks and Defences in Vision-Based Tasks: A SurveyPreprint

Sharma, A., Bian, Y., Munz, P., & Narayan, A. (2022) · doi:10.36227/techrxiv.20085902 · Cited by 14

A New Facial Authentication Pitfall and Remedy in Web Services

Cole, D. R., Newman, S., & Lin, D. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3067794 · Cited by 12

Backdoor Attacks with Wavelet Embedding: Revealing and enhancing the insights of vulnerabilities in visual object detection models on transformers within digital twin systems

Shen, M., & Huang, R. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102355 · Cited by 12

Detecting Trojan Attacks on Deep Neural Networks

Singh, J., & Sharmila, V. C. (2020) · International Conference on Computer, Communication and Signal Processing (ICCCSP) · doi:10.1109/icccsp49186.2020.9315256 · Cited by 5

Trojan Attacks and Defenses on Deep Neural Networks

Liu, Y. (2022) · Purdue e-Pubs (Purdue University System) · doi:10.25394/pgs.21312327

An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks

Tang, R., Du, M., Liu, N., Yang, F., & Hu, X. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403064 · Cited by 187

Programmable Neural Network Trojan for Pre-Trained Feature ExtractorPreprint

Ji, Y., Liu, Z., Hu, X., Wang, P., & Zhang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07766 · Cited by 18

MISA: Online Defense of Trojaned Models using Misattributions

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