Evasion Attacks against Machine Learning at Test Time

Adversarial machine learning

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194 · see citing works

Adversarial classification

Dalvi, N. N., Domingos, P. M., Mausam, Sanghai, S. K., & Verma, D. (2004) · Knowledge Discovery and Data Mining · doi:10.1145/1014052.1014066 · Cited by 1,085 · see citing works

Can machine learning be secure?

Barreno, M., Nelson, B. A., Sears, R., Joseph, A. D., & Tygar, J. D. (2006) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/1128817.1128824 · Cited by 852 · see citing works

Adversarial learning

Lowd, D., & Meek, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081950 · Cited by 755 · see citing works

Security Evaluation of Pattern Classifiers under Attack

Biggio, B., Fumera, G., & Roli, F. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.57 · Cited by 412 · see citing works

Nightmare at test time: robust learning by feature deletion

Globerson, A., & Roweis, S. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143889 · Cited by 309 · see citing works

Malicious PDF detection using metadata and structural features

Smutz, C., & Stavrou, A. (2012) · Proceedings of the 28th Annual Computer Security Applications Conference · doi:10.1145/2420950.2420987 · Cited by 271 · see citing works

Stackelberg games for adversarial prediction problems

Brückner, M., & Scheffer, T. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020495 · Cited by 229 · see citing works

Multiple classifier systems for robust classifier design in adversarial environments

Biggio, B., Fumera, G., & Roli, F. (2010) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-010-0007-7 · Cited by 187 · see citing works

Learning to classify with missing and corrupted features

Dekel, O., Shamir, O., & Xiao, L. (2009) · Machine Learning · doi:10.1007/s10994-009-5124-8 · Cited by 125 · see citing works

A Pattern Recognition System for Malicious PDF Files Detection

Maiorca, D., Giacinto, G., & Corona, I. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31537-4_40 · Cited by 101 · see citing works

Design of robust classifiers for adversarial environments

Biggio, B., Fumera, G., & Roli, F. (2011) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2011.6083796 · Cited by 39 · see citing works

Discriminative Direction for Kernel Classifiers

Golland, P. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0100 · Cited by 28 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

The Limitations of Deep Learning in Adversarial Settings

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993 · see citing works

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Papernot, N., McDaniel, P. D., Wu, X., Jha, S., & Swami, A. (2016) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2016.41 · Cited by 3,123 · see citing works

Universal Adversarial Perturbations

Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.17 · Cited by 2,680 · see citing works

Adversarial machine learning at scale

Kurakin, A., Goodfellow, I., & Bengio, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01236 · Cited by 2,325 · see citing works

Robust physical-world attacks on deep learning visual classification

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00175 · Cited by 2,173 · see citing works

Adversarial Examples Are Not Easily Detected

Carlini, N., & Wagner, D. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140444 · Cited by 1,626 · see citing works

Machine behaviour

Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364 · see citing works

Improving Transferability of Adversarial Examples With Input Diversity

Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., & Yuille, A. (2019) · 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/cvpr.2019.00284 · Cited by 1,356 · see citing works

Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

Dong, Y., Pang, T., Su, H., & Zhu, J. (2019) · 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/cvpr.2019.00444 · Cited by 1,034 · see citing works

Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser

Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., & Zhu, J. (2018) · 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/cvpr.2018.00191 · Cited by 967 · see citing works

Safety Verification of Deep Neural Networks

Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63387-9_1 · Cited by 859 · see citing works

Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection

Thys, S., Ranst, W. V., & Goedemé, T. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00012 · Cited by 713 · see citing works

Data collection and quality challenges in deep learning: a data-centric AI perspective

Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023) · The VLDB Journal · doi:10.1007/s00778-022-00775-9 · Cited by 573 · see citing works

Adversarial Examples for Malware Detection

Grosse, K., Papernot, N., Manoharan, P., Michael Backes, A., & McDaniel, P. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66399-9_4 · Cited by 566 · see citing works

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549 · see citing works

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works

Data-centric Artificial Intelligence: A Survey

Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2025) · ACM Computing Surveys · doi:10.1145/3711118 · Cited by 508 · see citing works

SoK: Security and Privacy in Machine Learning

Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. P. (2018) · 2018 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2018.00035 · Cited by 502 · see citing works

DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

Xie, X., Ma, L., Juefei-Xu, F., Xue, M., Chen, H., Liu, Y., Zhao, J., Li, B., Yin, J., & See, S. C. W. (2019) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3293882.3330579 · Cited by 440 · see citing works

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428 · see citing works

A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421 · see citing works

Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019) · IEEE Access · doi:10.1109/access.2019.2942390 · Cited by 421 · see citing works

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Ahmadi, M., Ulyanov, D., Semenov, S., Trofimov, M., & Giacinto, G. (2016) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2857705.2857713 · Cited by 380 · see citing works

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works

Practical Evasion of a Learning-Based Classifier: A Case Study

Šrndić, N., & Laskov, P. (2014) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2014.20 · Cited by 340 · see citing works

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Chen, X., Li, C., Wang, D., Wen, S., Zhang, J., Nepal‬, ‪., Xiang, Y., & Ren, K. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2932228 · Cited by 335 · see citing works

Adversarial Attack and Defense on Graph Data: A Survey

Sun, L., Dou, Y., Yang, C., Zhang, K., Wang, J., Yu, P. S., He, L., & Li, B. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3201243 · Cited by 331 · see citing works

Intriguing Properties of Adversarial ML Attacks in the Problem Space

Pierazzi, F., Pendlebury, F., Cortellazzi, J., & Cavallaro, L. (2020) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40000.2020.00073 · Cited by 326 · see citing works

Adversarial Attacks and Defences Competition

Kurakin, A., Goodfellow, I., Samy Bengio, A., Dong, Y., Liao, F., Liang, M., Pang, T., Zhu, J., Hu, X., Xie, C., et al. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-94042-7_11 · Cited by 321 · see citing works

Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

Zhong, H., Liao, C., Squicciarini, A., Zhu, S., & Miller, D. J. (2020) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3374664.3375751 · Cited by 306 · see citing works

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295 · see citing works

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

Demontis, A., Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., & Roli, F. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2700270 · Cited by 295 · see citing works

Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction

Liang, B., Li, H., Su, M., Li, X., Shi, W., & Wang, X. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2018.2874243 · Cited by 275 · see citing works

Enhancing robustness of machine learning systems via data transformations

Bhagoji, A. N., Cullina, D., Sitawarin, C., & Mittal, P. (2018) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2018.8362326 · Cited by 275 · see citing works

Trustworthy ai: A computational perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261 · see citing works

Pitfalls in Machine Learning for Computer Security

Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., & Rieck, K. (2024) · Communications of the ACM · doi:10.1145/3643456 · Cited by 254 · see citing works

Vulnerabilities in federated learning

Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248 · see citing works

Robustness via Curvature Regularization, and Vice Versa

Moosavi-Dezfooli, S.-M., Fawzi, A., Uesato, J., & Frossard, P. (2019) · 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/cvpr.2019.00929 · Cited by 245 · see citing works

Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks

Miller, D. J., Xiang, Z. J., & Kesidis, G. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2970615 · Cited by 238 · see citing works

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

Song, L., Shokri, R., & Mittal, P. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3354211 · Cited by 229 · see citing works

Membership Leakage in Label-Only Exposures

Li, Z., & Zhang, Y. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484575 · Cited by 226 · see citing works

Exploring Adversarial Examples in Malware Detection

Suciu, O., Coull, S. E., & Johns, J. M. (2019) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2019.00015 · Cited by 225 · see citing works

Feature-Guided Black-Box Safety Testing of Deep Neural Networks

Wicker, M., Huang, X., & Kwiatkowska, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-89960-2_22 · Cited by 223 · see citing works

Wild Patterns

Biggio, B., & Roli, F. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3264418 · Cited by 223 · see citing works

Support vector machines under adversarial label contamination

Huang, X. X., Biggio, B., Nelson, B. A., Xiao, H., Eckert, C. M., & Roli, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.081 · Cited by 220 · see citing works

Machine learning in cybersecurity: A review

Handa, A., Sharma, A., & Shukla, S. K. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1306 · Cited by 207 · see citing works

Adversarial attacks and defenses in explainable artificial intelligence: A survey

Baniecki, H., & Biecek, P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102303 · Cited by 204 · see citing works

A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response

Dunsin, D., Ghanem, M. C., Ouazzane, K., & Vassilev, V. (2024) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2023.301675 · Cited by 201 · see citing works

A general framework for adversarial examples with objectives

Sharif, M., Bhagavatula, S., Bauer, L., & Reiter, M. K. (2019) · ACM Transactions on Privacy and Security · doi:10.1145/3317611 · Cited by 200 · see citing works

The Robustness of Deep Networks: A Geometrical Perspective

Fawzi, A., Moosavi-Dezfooli, S.-M., & Frossard, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2740965 · Cited by 200 · see citing works

Sparse and Imperceivable Adversarial Attacks

Croce, F., & Hein, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00482 · Cited by 195 · see citing works

Adversarial example detection for DNN models: a review and experimental comparison

Aldahdooh, A., Hamidouche, W., Fezza, S. A., & Olivier Déforges, A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10125-w · Cited by 192 · see citing works

Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective

Machado, G. R., Silva, E., & Goldschmidt, R. R. (2021) · ACM Computing Surveys · doi:10.1145/3485133 · Cited by 186 · see citing works

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

Liu, D., Yu, R., & Su, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803770 · Cited by 179 · see citing works

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Zaman, S., Alhazmi, K., Aseeri, M. A., Ahmed, M. R., Khan, R. T., Kaiser, M. S., & Mahmud, M. (2021) · IEEE Access · doi:10.1109/access.2021.3089681 · Cited by 179 · see citing works

Adversary Resistant Deep Neural Networks with an Application to Malware Detection

Wang, Q., Guo, W., Zhang, K., Ororbia, A. G., Xing, X., Liu, X., & Giles, C. L. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098158 · Cited by 177 · see citing works

Semantic Adversarial Examples

Hosseini, H., & Poovendran, R. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00212 · Cited by 172 · see citing works

Model-Reuse Attacks on Deep Learning Systems

Ji, Y., Zhang, X., Ji, S., Luo, X., & Wang, T. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243757 · Cited by 170 · see citing works

Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review

Zhang, Z., Liu, M., Sun, M., Deng, R., Cheng, P., Niyato, D. T., Chow, M., & Chen, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3349381 · Cited by 170 · see citing works

Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

Liang, F., Hatcher, W. G., Liao, W., Gao, W., & Yu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2948912 · Cited by 166 · see citing works

Robot Operating System 2

DiLuoffo, V., Michalson, W. R., & Sunar, B. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418770011 · Cited by 165 · see citing works

Adversarial EXEmples

Demetrio, L., Coull, S. E., Biggio, B., Lagorio, G., Armando, A., & Roli, F. (2021) · ACM Transactions on Privacy and Security · doi:10.1145/3473039 · Cited by 158 · see citing works

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

Cinà, A. E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B. A., Oprea, A., Biggio, B., Pelillo, M., & Roli, F. (2023) · ACM Computing Surveys · doi:10.1145/3585385 · Cited by 152 · see citing works

A review of spam email detection: analysis of spammer strategies and the dataset shift problem

Jáñez-Martino, F., Aláiz-Rodríguez, R., González‐Castro, V., Fidalgo, E., & Alegre, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10195-4 · Cited by 145 · see citing works

A Survey on Trustworthy Recommender Systems

Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652891 · Cited by 144 · see citing works

SoK: Certified Robustness for Deep Neural Networks

Li, L., Xie, T., & Li, B. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179303 · Cited by 143 · see citing works

Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process

Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3016246 · Cited by 140 · see citing works

Poisoning behavioral malware clustering

Biggio, B., Rieck, K., Ariu, D., Wressnegger, C., Corona, I., Giacinto, G., & Roli, F. (2014) · doi:10.1145/2666652.2666666 · Cited by 139 · see citing works

AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope

Singh, A. P., Satapathy, S. C., Roy, A., & Gutub, A. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06348-2 · Cited by 139 · see citing works

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat, A. K., Mouli, V. R., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. (2020) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-020-00020-1 · Cited by 136 · see citing works

Machine Learning–based Cyber Attacks Targeting on Controlled Information

Miao, Y., Chen, C., Pan, L., Han, Q., Zhang, J., & Xiang, Y. (2021) · ACM Computing Surveys · doi:10.1145/3465171 · Cited by 133 · see citing works

Model Extraction Warning in MLaaS Paradigm

Kesarwani, M., Mukhoty, B., Arya, V., & Mehta, S. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274740 · Cited by 131 · see citing works

De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks

Chen, J., Zhang, X., Zhang, R., Wang, C., & Liu, L. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3080522 · Cited by 126 · see citing works

Addressing Adversarial Attacks Against Security Systems Based on Machine Learning

Apruzzese, G., Colajanni, M., Ferretti, L., & Marchetti, M. (2019) · doi:10.23919/cycon.2019.8756865 · Cited by 119 · see citing works

Stateful Detection of Black-Box Adversarial Attacks

Chen, S., Carlini, N., & Wagner, D. (2020) · Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligence · doi:10.1145/3385003.3410925 · Cited by 119 · see citing works

IoT Network Security from the Perspective of Adversarial Deep Learning

Sagduyu, Y. E., Shi, Y., & Erpek, T. (2019) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2019.8824956 · Cited by 118 · see citing works

Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks

Apruzzese, G., Andreolini, M., Marchetti, M., Venturi, A., & Colajanni, M. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3031843 · Cited by 117 · see citing works

Empirical Study of the Topology and Geometry of Deep Networks

Fawzi, A., Moosavi-Dezfooli, S.-M., Frossard, P., & Soatto, S. (2018) · 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/cvpr.2018.00396 · Cited by 117 · see citing works

Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems

Newaz, A. I., Haque, N. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322472 · Cited by 117 · see citing works

Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

Mandlekar, A., Zhu, Y., Garg, A., Fei-Fei, L., & Savarese, S. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206245 · Cited by 116 · see citing works

Security Evaluation of Support Vector Machines in Adversarial Environments

Biggio, B., Corona, I., Nelson, B. A., Rubinstein, B. I. P., Maiorca, D., Fumera, G., Giacinto, G., & Roli, F. (2014) · Springer eBooks · doi:10.1007/978-3-319-02300-7_4 · Cited by 116 · see citing works

Cryptanalytic Extraction of Neural Network Models

Carlini, N., Jagielski, M., & Mironov, I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-56877-1_7 · Cited by 116 · see citing works

Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses

Qayyum, A., Butt, M. A., Ali, H. M., Usman, M., Halabi, O., Al‐Fuqaha, A., Abbasi, Q. H., Imran, M. A., & Qadir, J. (2023) · ACM Computing Surveys · doi:10.1145/3614426 · Cited by 111 · see citing works

Malware Detection in Adversarial Settings

Yang, W. M., Kong, D., Xie, T., & Gunter, C. A. (2017) · Annual Computer Security Applications Conference · doi:10.1145/3134600.3134642 · Cited by 108 · see citing works

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur, A., & Gurusamy, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3129079 · Cited by 107 · see citing works

Deep k-NN Defense Against Clean-Label Data Poisoning Attacks

Peri, N., Gupta, N., Huang, W., Fowl, L., Zhu, C., Feizi, S., Goldstein, T., & Dickerson, J. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66415-2_4 · Cited by 107 · see citing works

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

Yan, S., Ren, J., Wang, W., Sun, L., Zhang, W., & Yu, Q. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3225137 · Cited by 107 · see citing works

Open Sesame! Universal Black-Box Jailbreaking of Large Language Models

Lapid, R., Langberg, R., & Sipper, M. (2024) · Applied Sciences · doi:10.3390/app14167150 · Cited by 107 · see citing works

Towards Adversarial Malware Detection

Maiorca, D., Biggio, B., & Giacinto, G. (2019) · ACM Computing Surveys · doi:10.1145/3332184 · Cited by 106 · see citing works

Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective

Biggio, B., Fumera, G., Russu, P., Didaci, L., & Roli, F. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2426728 · Cited by 105 · see citing works

Enhancing Knowledge Tracing via Adversarial Training

Guo, X., Huang, Z., Gao, J., Shang, M., Shu, M., & Sun, J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475554 · Cited by 105 · see citing works

REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data

Chen, X., Wang, W., Bender, C., Ding, Y., Jia, R., Li, B., & Song, D. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3453079 · Cited by 100 · see citing works

Assessing and Remedying Coverage for a Given Dataset

Asudeh, A., Jin, Z., & Jagadish, H. V. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00056 · Cited by 99 · see citing works

Deep Nets: What have They Ever Done for Vision?

Yuille, A. L., & Liu, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01405-z · Cited by 98 · see citing works

Evading Anti-Malware Engines With Deep Reinforcement Learning

Fang, Z., Wang, J., Li, B., Wu, S., Zhou, Y., & Huang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2908033 · Cited by 97 · see citing works

Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification

Khamaiseh, S. Y., Bagagem, D., Al-Alaj, A., Mancino, M., & Alomari, H. W. (2022) · IEEE Access · doi:10.1109/access.2022.3208131 · Cited by 97 · see citing works

Testing human ability to detect ‘deepfake’ images of human faces

Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023) · Journal of Cybersecurity · doi:10.1093/cybsec/tyad011 · Cited by 96 · see citing works

Feature Purification: How Adversarial Training Performs Robust Deep Learning

Allen-Zhu, Z., & Li, Y. (2022) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs52979.2021.00098 · Cited by 96 · see citing works

Attacking Recommender Systems with Augmented User Profiles

Lin, C., Chen, S., Li, H., Xiao, Y., Li, L., & Yang, Q. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411884 · Cited by 94 · see citing works

Learning a Secure Classifier against Evasion Attack

Khorshidpour, Z., Hashemi, S., & Hamzeh, A. (2016) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2016.0049 · Cited by 14 · see citing works

Strengthening Malware Classifiers: A Robustness Analysis Against Evasion Attacks Using Adversarial Training

Santosh, K. C., Fathima, N., Gupta, S., & Geetha Lakshmi, N. (2026) · International Research Journal on Advanced Engineering and Management (IRJAEM) · doi:10.47392/irjaem.2026.0282

Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers

Xu, W., Qi, Y., & Evans, D. (2016) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23115 · Cited by 222 · see citing works

Secure Kernel Machines against Evasion Attacks

Russu, P., Demontis, A., Biggio, B., Fumera, G., & Roli, F. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996771 · Cited by 71 · see citing works

‘Security Theater’: On the Vulnerability of Classifiers to Exploratory Attacks

Sethi, T. S., Kantardzic, M. M., & Ryu, J. W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57463-9_4 · Cited by 10 · see citing works