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

Defending Multimodal Fusion Models against Single-Source Adversaries

Yang, K., Lin, W.-Y., Barman, M., Condessa, F., & Kolter, J. Z. (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/cvpr46437.2021.00335 · Cited by 48

Collaborative Machine Learning: Schemes, Robustness, and Privacy

Wang, J., Pal, A., Yang, Q., Kant, K., Zhu, K., & Guo, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3169347 · Cited by 43

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Naresh, V. S., & Thamarai, M. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1490 · Cited by 45

Adversarial Patch Attacks on Monocular Depth Estimation Networks

Yamanaka, K., Matsumoto, R., Takahashi, K., & Fujii, T. (2020) · IEEE Access · doi:10.1109/access.2020.3027372 · Cited by 46

Design and Interpretation of Universal Adversarial Patches in Face Detection

Yang, X. H., Wei, F., Change Institutions to: University of Waterloo, & Zhu, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_11 · Cited by 41

Uncovering Lateral Movement Using Authentication Logs

Bian, H., Bai, T., Salahuddin, M. A., Limam, N., Daya, A. A., & Boutaba, R. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3054356 · Cited by 40

An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

Qiu, H., Zeng, Y., Zheng, Q., Guo, S., Zhang, T., & Li, H. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3076826 · Cited by 40

Insights into security and privacy issues in smart healthcare systems based on medical images

Yan, F., Li, N., Iliyasu, A. M., Salama, A. S., & Hirota, K. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103621 · Cited by 40

The Adversarial Robustness of Sampling

Ben‐Eliezer, O., & Yogev, E. (2020) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/3375395.3387643 · Cited by 48

Real-world Attack on MTCNN Face Detection System

Kaziakhmedov, E., Kireev, K., Melnikov, G., Pautov, M., & Petiushko, A. (2019) · International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) · doi:10.1109/sibircon48586.2019.8958122 · Cited by 38

Adversarial Examples on Power Systems State Estimation

Sayghe, A., Anubi, O. M., & Konstantinou, C. (2020) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt45199.2020.9087789 · Cited by 38

Provable Tradeoffs in Adversarially Robust Classification

Dobriban, E., Hassani, H., Hong, D. K., & Robey, A. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3205449 · Cited by 64

Quantifying and Mitigating Privacy Risks of Contrastive Learning

He, X., & Zhang, Y. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484571 · Cited by 39

Dual-Key Multimodal Backdoors for Visual Question Answering

Walmer, M., Sikka, K., Sur, I., Shrivastava, A., & Jha, S. (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/cvpr52688.2022.01494 · Cited by 38

Byzantine Machine Learning: A Primer

Guerraoui, R., Gupta, N., & Pinot, R. (2023) · ACM Computing Surveys · doi:10.1145/3616537 · Cited by 37

Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis

Yang, L., Rajab, M. E., Shami, A. A., & Muhaidat, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3376631 · Cited by 37

ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness

Theagarajan, R., Chen, M., Bhanu, B., & Zhang, 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.2019.00715 · Cited by 38

Revisiting model’s uncertainty and confidences for adversarial example detection

Aldahdooh, A., Hamidouche, W., & Olivier Déforges, A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03373-y · Cited by 40

Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

Patil, S., Varadarajan, V., Walimbe, D., Gulechha, S., Shenoy, S., Raina, A., & Kotecha, K. V. (2021) · Algorithms · doi:10.3390/a14100297 · Cited by 43

FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning

Khalid, F., Hanif, M. A., Rehman, S., Qadir, J., & Shafique, M. (2019) · Design, Automation, and Test in Europe · doi:10.23919/date.2019.8715141 · Cited by 37

Can machine learning model with static features be fooled: an adversarial machine learning approach

Taheri, R., Javidan, R., Shojafar, M., Vinod, P., & Conti, M. (2020) · Cluster Computing · doi:10.1007/s10586-020-03083-5 · Cited by 37

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

Imam, N. H., & Vassilakis, V. G. (2019) · Robotics · doi:10.3390/robotics8030050 · Cited by 36

Face-Off: Adversarial Face Obfuscation

Chandrasekaran, V., Gao, C., Tang, B., Fawaz, K., Jha, S., & Banerjee, S. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0032 · Cited by 40

Adversarial Robustness for Unsupervised Domain Adaptation

Awais, M., Zhou, F., Xu, H., Hong, L., Luo, P., Bae, S., & Li, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00845 · Cited by 36

Ethical Adversaries

Delobelle, P., Temple, P., Perrouin, G., Frénay‬, B., Heymans, P., & Berendt, B. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468513 · Cited by 39

Treant: training evasion-aware decision trees

Calzavara, S., Lucchese, C., Tolomei, G., Abebe, S. A., & Orlando, S. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00694-9 · Cited by 34

Amora: Black-box Adversarial Morphing Attack

Wang, R., Juefei-Xu, F., Guo, Q., Huang, Y., Xie, X., Ma, L., & Liu, Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413544 · Cited by 34

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Kulkarni, V., Gawali, M., & Kharat, A. T. (2021) · JMIR Medical Informatics · doi:10.2196/28776 · Cited by 36

Boosting training for PDF malware classifier via active learning

Li, Y., Wang, X., Shi, Z., Zhang, R., Xue, J., & Wang, Z. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22451 · Cited by 34

Query-Efficient Adversarial Attack With Low Perturbation Against End-to-End Speech Recognition Systems

Wang, S., Zhang, Z., Zhu, G., Zhang, X., Zhou, Y., & Huang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3222963 · Cited by 35

ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking

Xiang, C., Valtchanov, A., Mahloujifar, S., & Mittal, P. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179319 · Cited by 33

Zero-Shot Learning by Harnessing Adversarial Samples

Chen, Z., Zhang, P.-F., Li, J., Wang, S., & Huang, Z. H. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611823 · Cited by 34

Practical Attacks on Deep Neural Networks by Memory Trojaning

Hu, X., Zhao, Y. K., Deng, L., Liang, L., Zuo, P., Ye, J., Lin, Y., & Xie, Y. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.2995347 · Cited by 33

Scalable Polyhedral Verification of Recurrent Neural Networks

Ryou, W., Chen, J., Balunović, M., Singh, G., Dan, A., & Vechev, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-81685-8_10 · Cited by 35

Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions

Mengara, O., Avila, A. R., & Falk, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3355816 · Cited by 35

When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time

Miller, D. J., Wang, Y., & Kesidis, G. (2019) · Neural Computation · doi:10.1162/neco_a_01209 · Cited by 32

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors

Apruzzese, G., Andreolini, M., Marchetti, M., Colacino, V. G., & Russo, G. (2020) · Symmetry · doi:10.3390/sym12040653 · Cited by 32

Adversarial example generation with adabelief optimizer and crop invariance

Yang, B., Zhang, H., Li, Z., Zhang, Y., Xu, K., & Wang, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03469-5 · Cited by 32

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

Gubri, M., Cordy, M., Papadakis, M., Traon, Y. L., & Sen, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_35 · Cited by 32

A Comprehensive Review of Adversarial Attacks and Defense Strategies in Deep Neural Networks

Abomakhelb, A. I. A., Jalil, K. A., Buja, A. G., Alhammadi, A., & Alenezi, A. M. (2025) · Technologies · doi:10.3390/technologies13050202 · Cited by 34

On the Adversarial Robustness of LASSO Based Feature Selection

Li, F., Lai, L., & Cui, S. R. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3115943 · Cited by 31

A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images

Anelli, V. W., Deldjoo, Y., Di Noia, T., Malitesta, D., & Merra, F. A. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462848 · Cited by 31

Employing Deep Ensemble Learning for Improving the Security of Computer Networks Against Adversarial Attacks

Nowroozi, E., Mohammadi, M., Savaş, E., Mekdad, Y., & Conti, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3267831 · Cited by 30

Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus

Fleshman, W., Raff, E., Zak, R., McLean, M., & Nicholas, C. (2018) · International Conference on Malicious and Unwanted Software · doi:10.1109/malware.2018.8659360 · Cited by 30

On the hardness of evading combinations of linear classifiers

Stevens, D., & Lowd, D. (2013) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2517312.2517318 · Cited by 29

Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning

West, M. T., Nakhl, A. C., Heredge, J., Creevey, F. M., Hollenberg, L. C. L., Sevior, M. E., & Usman, M. (2024) · Intelligent Computing · doi:10.34133/icomputing.0100 · Cited by 38

Adversarial Training is Not Ready for Robot Learning

Lechner, M., Hasani, R., Grosu, R., Rus, D. L., & Henzinger, T. A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561036 · Cited by 29

Turning Federated Learning Systems Into Covert Channels

Costa, G., Pinelli, F., Soderi, S., & Tolomei, G. (2022) · IEEE Access · doi:10.1109/access.2022.3229124 · Cited by 35

Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability

Rosenberg, I., Meir, S., Berrebi, J., Gordon, I., Sicard, G., & David, E. O. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207168 · Cited by 29

Unjustified Classification Regions and Counterfactual Explanations in Machine Learning

Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., & Detyniecki, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-46147-8_3 · Cited by 28

Computer vision for plant pathology: A review with examples from cocoa agriculture

Sykes, J. R., Denby, K., & Franks, D. W. (2023) · Applications in Plant Sciences · doi:10.1002/aps3.11559 · Cited by 29

Adversarial robustness improvement for deep neural networks

Eleftheriadis, C., Symeonidis, A. L., & Katsaros, P. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01519-1 · Cited by 31

Trojan Attack and Defense for Deep Learning-Based Navigation Systems of Unmanned Aerial Vehicles

Mynuddin, M., Khan, S. U., Ahmari, R., Landivar, L., Nabil, M., & Homaifar, A. (2024) · IEEE Access · doi:10.1109/access.2024.3419800 · Cited by 29

Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures

Bagchi, S., Aggarwal, V., Chaterji, S., Douglis, F., Gamal, A. E., Han, J., Henz, B. J., Hoffmann, H., Jana, S., Kulkarni, M., et al. (2020) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2020.3006807 · Cited by 28

Auditing Membership Leakages of Multi-Exit Networks

Li, Z., Liu, Y., He, X., Yu, N., Michael Backes, A., & Zhang, Y. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3559359 · Cited by 29

On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach

Anisetti, M., Ardagna, C. A., Balestrucci, A., Bena, N., Damiani, E., & Yeun, C. Y. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3293269 · Cited by 29

CAPTCHA Image Generation Systems Using Generative Adversarial Networks

Kwon, H., Kim, Y., Yoon, H., & Choi, D. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017edl8175 · Cited by 28

Attacking Automatic Video Analysis Algorithms

Hosseini, H., Xiao, B., Clark, A., & Poovendran, R. (2017) · doi:10.1145/3137616.3137618 · Cited by 28

The Naked Sun: Malicious Cooperation Between Benign-Looking Processes

Gaspari, F. D., Hitaj, D., Pagnotta, G., Carli, L. D., & Mancini, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57878-7_13 · Cited by 32

Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks

Li, X., & Ji, S. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43887-6_15 · Cited by 28

Toward an Understanding of Adversarial Examples in Clinical Trials

Papangelou, K., Sechidis, K., Weatherall, J., & Gavin Brown, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_3 · Cited by 27

Minimum-Norm Adversarial Examples on KNN and KNN based Models

Sitawarin, C., & Wagner, D. (2020) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw50608.2020.00023 · Cited by 26

A Survey on Efficient Methods for Adversarial Robustness

Muhammad, A., & Bae, S. (2022) · IEEE Access · doi:10.1109/access.2022.3216291 · Cited by 34

Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

Pozdnyakov, V., Kovalenko, A., Makarov, I., Drobyshevskiy, M., & Lukyanov, K. S. (2024) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2024.3401396 · Cited by 27

A game-theoretic defense against data poisoning attacks in distributed support vector machines

Zhang, R., & Zhu, Q. (2017) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2017.8264336 · Cited by 26

On Security and Sparsity of Linear Classifiers for Adversarial Settings

Demontis, A., Russu, P., Biggio, B., Fumera, G., & Roli, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49055-7_29 · Cited by 27

Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks

Ali, M., Hu, Y. F., Luong, D. K., Oguntala, G., Li, J.-P., & Abdo, K. (2020) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc50938.2020.9256597 · Cited by 25

Adversarial Attacks Against IoT Identification Systems

Kotak, J., & Elovici, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3229906 · Cited by 25

Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact

Sen, J., & Dasgupta, S. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.112442 · Cited by 32

LUNA: A Model-Based Universal Analysis Framework for Large Language Models

Song, D., Xie, X., Song, J., Zhu, D., Huang, Y., Juefei-Xu, F., & Ma, L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3411928 · Cited by 33

A gradient-based pixel-domain attack against SVM detection of global image manipulations

Chen, Z., Tondi, B., Li, X., Ni, R., Zhao, Y., & Barni, M. (2017) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2017.8267668 · Cited by 25

Contextual Identification of Windows Malware through Semantic Interpretation of API Call Sequence

Amer, E., El–Sappagh, S., & Hu, J. W. (2020) · Applied Sciences · doi:10.3390/app10217673 · Cited by 25

Increasing the Confidence of Deep Neural Networks by Coverage Analysis

Rossolini, G., Biondi, A., & Buttazzo, G. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3163682 · Cited by 27

Morphence: Moving Target Defense Against Adversarial Examples

Amich, A., & Eshete, B. (2021) · Annual Computer Security Applications Conference · doi:10.1145/3485832.3485899 · Cited by 24

Physical Adversarial Attack on a Robotic Arm

Jia, Y., POSKITT, C. M., Sun, J., & Chattopadhyay, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3189783 · Cited by 24

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux, Q. L., Bourbao, E., Teglia, Y., & Kallas, K. (2024) · IEEE Access · doi:10.1109/access.2024.3382584 · Cited by 30

CARLA-GeAR: A Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Deep Learning Vision Models

Nesti, F., Rossolini, G., D’Amico, G., Biondi, A., & Buttazzo, G. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3412432 · Cited by 24

From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing

Cen, S. H., & Alur, R. (2024) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3689904.3694711 · Cited by 35

Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems

Xie, S., Wang, H., Kong, Y., & Hong, Y. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833776 · Cited by 24

Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet

Neuhaus, Y., Augustin, M., Boreiko, V., & Hein, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01851 · Cited by 30

Evaluation of random forest classifier in security domain

Khorshidpour, Z., Hashemi, S., & Hamzeh, A. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0907-2 · Cited by 23

An Overview of Robot Operating System Forensics

Basheer, M. M., & Varol, A. (2019) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/ubmyk48245.2019.8965649 · Cited by 25

Robust Pervasive Detection for Adversarial Samples of Artificial Intelligence in IoT Environments

Wang, S., & Qiao, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2919695 · Cited by 23

Spectral Leakage and Rethinking the Kernel Size in CNNs

Tömen, N., & van Gemert, J. C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00509 · Cited by 23

Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

Wang, Z., Huang, C., & Zhu, Q. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774719 · Cited by 24

Rethinking Label Flipping Attack: From Sample Masking to Sample Thresholding

Xu, Q., Yang, Z., Zhao, Y., Cao, X., & Huang, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3220849 · Cited by 22

A Wolf in Sheep’s Clothing: Query-Free Evasion Attacks Against Machine Learning-Based Malware Detectors with Generative Adversarial Networks

Gibert, D., Planes, J., Le, Q., & Zizzo, G. (2023) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw59978.2023.00052 · Cited by 22

Adversarial Obstacle Generation Against LiDAR-Based 3D Object Detection

Wang, J., Li, F., Zhang, X., & Sun, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3302018 · Cited by 22

Pretzel

Gupta, T., Fingler, H., Alvisi, L., & Walfish, M. (2017) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3098822.3098835 · Cited by 22

Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures

Oregi, I., Ser, J. D., Pérez, A., & Lozano, J. A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-99626-4_3 · Cited by 22

Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks

Chen, J., Wu, X., Rastogi, V., Liang, Y., & Jha, S. (2019) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2019.00042 · Cited by 24

Adversary-Aware Multimodal Neural Networks for Cancer Susceptibility Prediction From Multiomics Data

Karim, M. R., Islam, T., Lange, C., Rebholz‐Schuhmann, D., & Decker, S. (2022) · IEEE Access · doi:10.1109/access.2022.3175816 · Cited by 24

QoE-Driven Adaptive Video Streaming: Architectures, Techniques, and Future Research Challenges Toward 6G Networks

Alsader, M., Barakabitze, A. A., & Mkwawa, I.-H. M. (2025) · IEEE Access · doi:10.1109/access.2025.3597058 · Cited by 21

Omni: automated ensemble with unexpected models against adversarial evasion attack

Shu, R., Xia, T., Williams, L. A., & Menzies, T. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-10064-8 · Cited by 23

Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

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Secure Kernel Machines against Evasion Attacks

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