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

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

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

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

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

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

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

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

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

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 126

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 126

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

ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining

Chen, J., Li, Y., Wu, X., Liang, Y., & Jha, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86523-8_26 · Cited by 94

ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles

Zhang, J., Xu, C., & Li, B. (2024) · 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/cvpr52733.2024.01464 · Cited by 117

“Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice

Apruzzese, G., Anderson, H. S., Dambra, S., Freeman, D. M., Pierazzi, F., & Roundy, K. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00031 · Cited by 120

Semantic Robustness of Models of Source Code

Henkel, J., Ramakrishnan, G., Wang, Z., Albarghouthi, A., Jha, S., & Reps, T. (2022) · 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00070 · Cited by 92

Adversarial Machine Learning

Vorobeychik, Y., & Kantarcıoğlu, M. (2018) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00861ed1v01y201806aim039 · Cited by 92

Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-linear Activations

Sharmin, S., Rathi, N., Panda, P., & Roy, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_24 · Cited by 94

Adversarial Attacks Against Deep Generative Models on Data: A Survey

Sun, H., Zhu, T., Zhang, Z., Jin, D., Xiong, P., & Zhou, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130903 · Cited by 90

Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems

Erba, A., Taormina, R., Galelli, S., Pogliani, M., Carminati, M., Zanero, S., & Tippenhauer, N. O. (2020) · Annual Computer Security Applications Conference · doi:10.1145/3427228.3427660 · Cited by 97

Subpopulation Data Poisoning Attacks

Jagielski, M., Severi, G., Pousette Harger, N., & Oprea, A. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3485368 · Cited by 86

Ask, Acquire, and Attack: Data-Free UAP Generation Using Class Impressions

Mopuri, K. R., Uppala, P. K., & Babu, R. V. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01240-3_2 · Cited by 86

ZSM Security: Threat Surface and Best Practices

Benzaïd, C., & Taleb, T. (2020) · IEEE Network · doi:10.1109/mnet.001.1900273 · Cited by 86

Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey

McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2010010 · Cited by 86

Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

Narsimha, B., Raghavendran, C. V., Rajyalakshmi, P., Reddy, G. K., Bhargavi, M., & Naresh, P. (2022) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.100206 · Cited by 85

A System-Driven Taxonomy of Attacks and Defenses in Adversarial Machine Learning

Sadeghi, K., Banerjee, A., & Gupta, S. K. S. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.2968933 · Cited by 87

Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning

Rathore, H., Sahay, S. K., Nikam, P., & Sewak, M. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10083-8 · Cited by 83

Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures)

Mirzaee, P. H., Shojafar, M., Cruickshank, H. S., & Tafazolli, R. (2022) · IEEE Access · doi:10.1109/access.2022.3174259 · Cited by 83

Towards Evaluation of NIDSs in Adversarial Setting

Hashemi, M., Cusack, G., & Keller, E. R. (2019) · doi:10.1145/3359992.3366642 · Cited by 81

DeltaPhish: Detecting Phishing Webpages in Compromised Websites

Corona, I., Biggio, B., Contini, M., Piras, L., Corda, R., Mereu, M., Mureddu, G., Ariu, D., & Roli, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66402-6_22 · Cited by 83

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid

Melis, M., Demontis, A., Biggio, B., Gavin Brown, B., Fumera, G., & Roli, F. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.94 · Cited by 85

Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

Lucas, K., Sharif, M., Bauer, L., Reiter, M. K., & Shintre, S. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3453086 · Cited by 80

Towards quantum enhanced adversarial robustness in machine learning

West, M. T., Tsang, S.-L., Low, J. S., Hill, C. D., Leckie, C., Hollenberg, L. C. L., Erfani, S. M., & Usman, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00661-1 · Cited by 79

Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs

Aiken, J., & Scott-Hayward, S. (2019) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn47374.2019.9040101 · Cited by 79

Towards characterizing adversarial defects of deep learning software from the lens of uncertainty

Zhang, X., Xie, X., Ma, L., Du, X., Hu, Q., Liu, Y., Zhao, J., & Sun, M. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380368 · Cited by 84

Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting

Zou, J., Pan, Z., Qiu, J., Liu, X., Rui, T., & Li, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_34 · Cited by 84

SoK: Explainable Machine Learning for Computer Security Applications

Nadeem, A., Vos, D., Cao, C., Pajola, L., Dieck, S., Baumgartner, R., & Verwer, S. (2023) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp57164.2023.00022 · Cited by 79

Automatically synthesizing DoS attack traces using generative adversarial networks

Yan, Q., Wang, M., Huang, W., Luo, X., & Yu, F. R. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-00925-6 · Cited by 79

Practical Data Poisoning Attack against Next-Item Recommendation

Zhang, H., Li, Y., Ding, B., & Gao, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3379992 · Cited by 77

Membership Inference Attacks Against Recommender Systems

Zhang, M., Ren, Z., Wang, Z., Ren, P., Chen, Z., Hu, P., & Zhang, Y. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3484770 · Cited by 76

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Ma, Y., Xie, T., Li, J., & Maciejewski, R. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934631 · Cited by 77

Evasion Attacks with Adversarial Deep Learning Against Power System State Estimation

Sayghe, A., Zhao, J., & Konstantinou, C. (2020) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm41954.2020.9281719 · Cited by 75

Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection

Wang, H., Muñoz-González, L., Eklund, D., & Raza, S. (2021) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3448300.3467827 · Cited by 75

Security risks and countermeasures of adversarial attacks on AI-driven applications in 6G networks: A survey

Hoang, V.-T., Ergu, Y. A., Nguyen, V.-L., & Chang, R. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104031 · Cited by 80

Analyzing the Robustness of Open-World Machine Learning

Sehwag, V., Bhagoji, A. N., Song, L., Sitawarin, C., Cullina, D., Chiang, M., & Mittal, P. (2019) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3338501.3357372 · Cited by 78

An Investigation of Cyber-Attacks and Security Mechanisms for Connected and Autonomous Vehicles

Gupta, S., Maple, C. R., & Passerone, R. (2023) · IEEE Access · doi:10.1109/access.2023.3307473 · Cited by 74

Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

Co, K. T., Muñoz-González, L., de Maupeou, S., & Lupu, E. C. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3345660 · Cited by 79

Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

Quiring, E., & Rieck, K. (2020) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw50608.2020.00024 · Cited by 74

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

Adversarial Machine Learning

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548 · Cited by 71

Randomized Prediction Games for Adversarial Machine Learning

Bulò, S. R., Biggio, B., Pillai, I., Pelillo, M., & Roli, F. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2593488 · Cited by 72

A Framework for Enhancing Deep Neural Networks Against Adversarial Malware

Li, D., Li, Q., Ye, Y., & Xu, S. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3051354 · Cited by 74

Simulating Unknown Target Models for Query-Efficient Black-box Attacks

Ma, C., Chen, L., & Yong, J. (2021) · 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/cvpr46437.2021.01166 · Cited by 69

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

Zhang, X., Zhou, Y., Pei, S., Zhuge, J., & Chen, J. (2020) · IEEE Access · doi:10.1109/access.2020.2965184 · Cited by 69

Block-Wise Image Transformation With Secret Key for Adversarially Robust Defense

MaungMaung, A., & Kiya, H. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3062977 · Cited by 72

NPC: N euron P ath C overage via Characterizing Decision Logic of Deep Neural Networks

Xie, X., Li, T., Wang, J., Ma, L., Guo, Q., Juefei-Xu, F., & Liu, Y. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3490489 · Cited by 70

A survey of game theoretic approach for adversarial machine learning

Zhou, Y., Kantarcıoğlu, M., & Xi, B. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1259 · Cited by 66

Single-Step Adversarial Training With Dropout Scheduling

Vivek, B. S., & Babu, R. V. (2020) · 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/cvpr42600.2020.00103 · Cited by 64

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning

Usynin, D., Ziller, A., Makowski, M. R., Braren, R., Rueckert, D., Glocker, B., Kaissis, G. A., & Passerat‐Palmbach, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00390-3 · Cited by 68

A Detailed Survey on Federated Learning Attacks and Defenses

Sikandar, H. S., Waheed, H., Tahir, S., Malik, S. U. R., & Rafique, W. (2023) · Electronics · doi:10.3390/electronics12020260 · Cited by 64

Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

Sayles, A., Hooda, A., Gupta, M., Chatterjee, R., & Fernandes, E. (2021) · 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/cvpr46437.2021.01443 · Cited by 68

Neural ODE Control for Classification, Approximation, and Transport

Ruiz-Balet, D., & Zuazua, E. (2023) · SIAM Review · doi:10.1137/21m1411433 · Cited by 84

Cracking Classifiers for Evasion

Liang, B., Su, M., You, W., Shi, W., & Yang, G. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883060 · Cited by 63

Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles

Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Tuckute, G., Feather, J., Boebinger, D., & McDermott, J. H. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002366 · Cited by 78

Adversarial Multimedia Forensics: Overview and Challenges Ahead

Barni, M., Stamm, M. C., & Tondi, B. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553305 · Cited by 62

Deep neural rejection against adversarial examples

Sotgiu, A., Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., & Roli, F. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00105-y · Cited by 68

WAF-A-MoLE

Demetrio, L., Valenza, A., Costa, G., & Lagorio, G. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373962 · Cited by 63

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Guesmi, A., Hanif, M. A., Ouni, B., & Shafique, M. (2023) · IEEE Access · doi:10.1109/access.2023.3321118 · Cited by 75

On the Robustness of Large Multimodal Models Against Image Adversarial Attacks

Cui, X., Aparcedo, A., Jang, Y. K., & Lim, S.-N. (2024) · 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/cvpr52733.2024.02325 · Cited by 76

Adversarial Detection with Model Interpretation

Liu, N., Yang, H., & Hu, X. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220027 · Cited by 61

Adversarial Examples Detection in Features Distance Spaces

Carrara, F., Becarelli, R., Caldelli, R., Falchi, F., & Amato, G. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_26 · Cited by 62

Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip

Chen, W., Zhang, Z., Hu, X., & Wu, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_17 · Cited by 64

Model metamers reveal divergent invariances between biological and artificial neural networks

Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01442-0 · Cited by 59

RHMD

Khasawneh, K. N., Abu‐Ghazaleh, N., Ponomarev, D. V., & Yu, L. (2017) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3123939.3123972 · Cited by 59

Forward Stability of ResNet and Its Variants

Zhang, L., & Schaeffer, H. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00922-y · Cited by 61

Query-Efficient Black-Box Adversarial Attacks Guided by a Transfer-Based Prior

Dong, Y., Cheng, S., Pang, T., Su, H., & Zhu, J. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126733 · Cited by 66

SysDroid: a dynamic ML-based android malware analyzer using system call traces

Ananya, A., Aswathy, A., Amal, T., Swathy, P., Vinod, P., & Shojafar, M. (2020) · Cluster Computing · doi:10.1007/s10586-019-03045-6 · Cited by 58

Decision-based evasion attacks on tree ensemble classifiers

Zhang, F., Wang, Y., Liu, S., & Wang, H. (2020) · World Wide Web · doi:10.1007/s11280-020-00813-y · Cited by 60

Applications of machine learning in cryptography

Alani, M. M. (2019) · International Conference on Cryptography, Security and Privacy (CSP) · doi:10.1145/3309074.3309092 · Cited by 57

Arms Race in Adversarial Malware Detection: A Survey

Li, D., Li, Q., Ye, Y., & Xu, S. (2021) · ACM Computing Surveys · doi:10.1145/3484491 · Cited by 57

Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey

Zhang, Q., Xin, C., & Wu, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058638 · Cited by 53

A Survey on Adversarial Attacks for Malware Analysis

Aryal, K., Gupta, M., Abdelsalam, M., Kunwar, P., & Thuraisingham, B. (2024) · IEEE Access · doi:10.1109/access.2024.3519524 · Cited by 58

Adversarial attacks on deep learning models in smart grids

Hao, J., & Tao, Y. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.026 · Cited by 55

Adversarial examples for network intrusion detection systems

Sheatsley, R., Papernot, N., Weisman, M. J., Verma, G., & McDaniel, P. D. (2022) · Journal of Computer Security · doi:10.3233/jcs-210094 · Cited by 59

FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments

Chernikova, A., & Oprea, A. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3544746 · Cited by 52

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing

Moore, E., Imteaj, A., Rezapour, S., & Amini, M. H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3313055 · Cited by 52

On the Robustness of Mobile Device Fingerprinting

Hupperich, T., Maiorca, D., Kührer, M., Holz, T., & Giacinto, G. (2015) · Annual Computer Security Applications Conference · doi:10.1145/2818000.2818032 · Cited by 52

A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks

Dasgupta, P., & Collins, J. B. (2019) · AI Magazine · doi:10.1609/aimag.v40i2.2847 · Cited by 60

An Overview of Vehicular Cybersecurity for Intelligent Connected Vehicles

Guan, T., Han, Y. H., Kang, N., Tang, N., Chen, X., & Wang, S. (2022) · Sustainability · doi:10.3390/su14095211 · Cited by 51

Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors

Apruzzese, G., Colajanni, M., & Marchetti, M. (2019) · IEEE International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca.2019.8935039 · Cited by 51

Adversarial Shape Perturbations on 3D Point Clouds

Liu, D., Yu, R., & Su, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66415-2_6 · Cited by 51

A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems

Sharma, A., Singh, S., Chhabra, A., Kumar, S., Arya, V., & Moslehpour, M. (2023) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.334711 · Cited by 49

Evasion and causative attacks with adversarial deep learning

Shi, Y., & Sagduyu, Y. E. (2017) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2017.8170807 · Cited by 52

APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection

Braunegg, A., Chakraborty, A., Krumdick, M., Lape, N., Leary, S., Manville, K., Merkhofer, E., Strickhart, L., & Walmer, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_3 · Cited by 57

A survey on privacy inference attacks and defenses in cloud-based Deep Neural Network

Zhang, X., Chen, C., Xie, Y., Chen, X., Zhang, J., & Xiang, Y. (2022) · Computer Standards & Interfaces · doi:10.1016/j.csi.2022.103672 · Cited by 53

Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware

Maiorca, D., & Biggio, B. (2019) · IEEE Security & Privacy · doi:10.1109/msec.2018.2875879 · Cited by 48

Robustifying Reinforcement Learning Agents via Action Space Adversarial Training

Tan, K. L., Esfandiari, Y., Lee, X. Y., Aakanksha, A., & Sarkar, S. (2020) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc45564.2020.9147846 · Cited by 48

Evading behavioral classifiers: a comprehensive analysis on evading ransomware detection techniques

Gaspari, F. D., Hitaj, D., Pagnotta, G., Carli, L. D., & Mancini, L. V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07096-6 · Cited by 53

One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time

Biggio, B., Corona, I., He, Z.-M., Chan, P. P. K., Giacinto, G., Yeung, D. S., & Roli, F. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-20248-8_15 · Cited by 47

The analysis of security and privacy risks in smart education environments

Kamenskih, A. N. (2022) · Journal of Smart Cities and Society · doi:10.3233/scs-210114 · Cited by 51

Adversarial Attacks and Defenses

Liu, N., Du, M., Guo, R., Liu, H., & Hu, X. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468519 · Cited by 56

Adversarial Vision Challenge

Brendel, W., Rauber, J., Kurakin, A., Papernot, N., Veliqi, B., Mohanty, S. P., Laurent, F., Salathé, M., Bethge, M., Yu, Y., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_5 · Cited by 50

Gray-Box Adversarial Training

Vivek, B. S., Mopuri, K. R., & Babu, R. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_13 · Cited by 46

Robustness Verification of Classification Deep Neural Networks via Linear Programming

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