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

MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion Attacks

Zhou, Y., Cheng, G., Yu, S., Chen, Z., & Hu, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3414339 · Cited by 20

Quantum neural networks under depolarization noise: exploring white-box attacks and defenses

Winderl, D., Franco, N., & Lorenz, J. M. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00208-6 · Cited by 23

Are You Tampering with My Data?

Alberti, M., Pondenkandath, V., Würsch, M., Bouillon, M., Seuret, M., Ingold, R., & Liwicki, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_25 · Cited by 20

Vulnerability Detection and Analysis in Adversarial Deep Learning

Shi, Y., Sagduyu, Y. E., Davaslıoğlu, K., & Levy, R. (2018) · Computer communications and networks · doi:10.1007/978-3-319-92624-7_9 · Cited by 20

Defending Against Machine Learning Based Inference Attacks via Adversarial Examples: Opportunities and Challenges

Jia, J., & Gong, N. Z. (2020) · Springer eBooks · doi:10.1007/978-3-030-33432-1_2 · Cited by 21

Search-based adversarial testing and improvement of constrained credit scoring systems

Ghamizi, S., Cordy, M., Gubri, M., Papadakis, M., Boystov, A., Traon, Y. L., & Goujon, A. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409739 · Cited by 20

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

Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model

Vijayaraghavan, P., & Roy, D. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46147-8_43 · Cited by 19

Deceiving Portable Executable Malware Classifiers into Targeted Misclassification with Practical Adversarial Examples

Kucuk, Y., & Yan, G. (2020) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3374664.3375741 · Cited by 19

On the Feasibility of Adversarial Sample Creation Using the Android System API

Cara, F., Scalas, M., Giacinto, G., & Maiorca, D. (2020) · Information · doi:10.3390/info11090433 · Cited by 19

Adversarial Detection by Latent Style Transformations

Wang, S., Nepal‬, ‪., Abuadbba, A., Rudolph, C., & Grobler, M. M. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3155975 · Cited by 19

Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments

Queyrut, S., Schiavoni, V., & Felber, P. (2023) · 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00069 · Cited by 18

Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems

Meyers, C., Löfstedt, T., & Elmroth, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10521-4 · Cited by 18

Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study

Xu, Y. S., Li, D., Li, Q., & Xu, S. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010005 · Cited by 20

StrategyAtlas: Strategy Analysis for Machine Learning Interpretability

Collaris, D., & van Wijk, J. J. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3146806 · Cited by 18

On the Robustness to Adversarial Examples of Neural ODE Image Classifiers

Carrara, F., Caldelli, R., Falchi, F., & Amato, G. M. (2019) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs47025.2019.9035109 · Cited by 18

Explainable Artificial Intelligence in Genomic Sequence for Healthcare Systems Prediction

Awotunde, J. B., Adeniyi, A. E., Ajamu, G. J., Balogun, G. B., & Taofeek-Ibrahim, F. A. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-97929-4_19 · Cited by 19

XAI enhancing cyber defence against adversarial attacks in industrial applications

Makridis, G., Theodoropoulos, S., Dardanis, D., Makridis, I., Separdani, M. M., Fatouros, G., Kyriazis, D., & Koulouris, P. (2022) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas55744.2022.10052858 · Cited by 17

Adversarial Data Augmentation for HMM-Based Anomaly Detection

Castellini, A., Masillo, F., Azzalini, D., Francesco Amigoni, A., & Farinelli, A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3303099 · Cited by 17

Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems

Huang, P., Gönültaş, E., Arnold, M., Srinath, K. P., Hoydis, J., & Studer, C. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3355288 · Cited by 21

Adversarial robustness guarantees for quantum classifiers

Dowling, N., West, M. T., Southwell, A., Nakhl, A. C., Sevior, M. E., Usman, M., & Modi, K. K. (2026) · npj Quantum Information · doi:10.1038/s41534-025-01129-3 · Cited by 34

A Study on Adversarial Sample Resistance and Defense Mechanism for Multimodal Learning-Based Phishing Website Detection

Duy, P. T., Minh, V. Q., Dang, B. T. H., Son, N. D. H., Quyen, N. H., & Pham, V.-H. (2024) · IEEE Access · doi:10.1109/access.2024.3436812 · Cited by 17

Threat Modeling AI/ML With the Attack Tree

Hoseini, S. V., Suutala, J., Partala, J., & Halunen, K. (2024) · IEEE Access · doi:10.1109/access.2024.3497011 · Cited by 22

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates

Angioni, D., Demetrio, L., Pintor, M., Oneto, L., Anguita, D., Biggio, B., & Roli, F. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3573237 · Cited by 17

How to implement MCDM tools and continuous logic into neural computation?

Csiszár, O., Csiszár, G., & Dombi, J. D. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106530 · Cited by 17

Crafting Adversarial Example to Bypass Flow-&ML- based Botnet Detector via RL

Wang, J., Qixu, L., Di, W., Dong, Y., & Cui, X. (2021) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3471621.3471841 · Cited by 17

A Comparative Study on the Impact of Adversarial Machine Learning Attacks on Contemporary Intrusion Detection Datasets

Pujari, M., Pacheco, Y., Cherukuri, B. P., & Sun, W. (2022) · SN Computer Science · doi:10.1007/s42979-022-01321-8 · Cited by 17

Understanding adversarial attacks on observations in deep reinforcement learning

Qiaoben, Y., Ying, C., Zhou, X., Su, H., Zhu, J., & Zhang, B. (2024) · Science China Information Sciences · doi:10.1007/s11432-021-3688-y · Cited by 20

IntelliAV: Toward the Feasibility of Building Intelligent Anti-malware on Android Devices

Ahmadi, M., Sotgiu, A., & Giacinto, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66808-6_10 · Cited by 17

Venomave: Targeted Poisoning Against Speech Recognition

Aghakhani, H., Schönherr, L., Eisenhofer, T., Kolossa, D., Holz, T., Kruegel, C., & Vigna, G. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00035 · Cited by 16

Toward Certified Robustness Against Real-World Distribution Shifts

Wu, H., Tagomori, T., Robey, A., Yang, F., Matni, N., Pappas, G. J., Hassani, H., Păsăreanu, C. S., & Barrett, C. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00042 · Cited by 28

Adversarial Patterns: Building Robust Android Malware Classifiers

Bhusal, D., & Rastogi, N. (2025) · ACM Computing Surveys · doi:10.1145/3717607 · Cited by 16

Security and Privacy Challenges of AIGC in Metaverse: A Comprehensive Survey

Zhang, S., Li, H., Sun, K., Chen, H., Wang, Y., & Li, S. (2025) · ACM Computing Surveys · doi:10.1145/3729419 · Cited by 17

Secure Deep Learning Engineering: A Road Towards Quality Assurance of Intelligent Systems

Liu, Y., Ma, L., & Zhao, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32409-4_1 · Cited by 16

Targeted Attack of Deep Hashing Via Prototype-Supervised Adversarial Networks

Zhang, Z., Wang, X., Lu, G., Shen, F., & Zhu, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3097506 · Cited by 16

Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks

Alarab, I., & Prakoonwit, S. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10707-3 · Cited by 16

Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method

Shi, L., Liao, T., & He, J. (2022) · Electronics · doi:10.3390/electronics11121814 · Cited by 16

Runtime Verification for Anomaly Detection of Robotic Systems Security

Kırca, Y. S., Değirmenci, E., Demirci, Z., Yazıcı, A. T., Özkan, M., Ergün, S., & Kanak, A. (2023) · Machines · doi:10.3390/machines11020166 · Cited by 16

Detecting Selected Network Covert Channels Using Machine Learning

Chourib, M. (2019) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcs48598.2019.9188115 · Cited by 16

Universal Adversarial Robustness of Texture and Shape-Biased Models

Co, K. T., Muñoz-González, L., Kanthan, L., Glocker, B., & Lupu, E. C. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506325 · Cited by 16

Gradient Correlation: Are Ensemble Classifiers More Robust Against Evasion Attacks in Practical Settings?

Zhang, F., Wang, Y., & Wang, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02922-7_7 · Cited by 15

Fraud Detection under Siege: Practical Poisoning Attacks and Defense Strategies

Paladini, T., Monti, F., Polino, M., Carminati, M., & Zanero, S. (2023) · ACM Transactions on Privacy and Security · doi:10.1145/3613244 · Cited by 15

Data Poisoning Attacks With Hybrid Particle Swarm Optimization Algorithms Against Federated Learning in Connected and Autonomous Vehicles

Cui, C., Du, H., Jia, Z., Zhang, X., He, Y., & Yang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3337638 · Cited by 16

TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization

Liu, Y., Feng, X., Wang, Y., Yang, W., & Ming, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00439 · Cited by 15

Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing

Gibert, D., Zizzo, G., Le, Q., & Planes, J. (2024) · IEEE Access · doi:10.1109/access.2024.3392391 · Cited by 15

Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems

Elnawawy, M., Hallajiyan, M., Mitra, G., Iqbal, S., & Pattabiraman, K. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00019 · Cited by 15

ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features

Cheng, P., Wang, Y., Huang, P., Ba, Z., Lin, X., Lin, F., Lü, L., & Ren, K. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00056 · Cited by 18

Exploring Research and Tools in AI Security: A Systematic Mapping Study

Narula, S., GhasemiGol, M., Carnerero-Cano, J., Minnich, A., Lupu, E., & Takabi, D. (2025) · IEEE Access · doi:10.1109/access.2025.3567195 · Cited by 21

Security and Functional Safety for AI in Embedded Automotive System—A Tutorial

Wang, Y., Xiao, J. Z., Wei, Z., Zheng, Y., Tang, K., & Chang, C.-H. (2023) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2023.3334273 · Cited by 16

Copying Machine Learning Classifiers

Unceta, I., Nin, J., & Pujol, O. (2020) · IEEE Access · doi:10.1109/access.2020.3020638 · Cited by 15

Noise as a Resource for Learning in Knowledge Distillation

Arani, E., Sarfraz, F., & Zonooz, B. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00317 · Cited by 17

Cloak & Co-locate: Adversarial Railroading of Resource Sharing-based Attacks on the Cloud

Makrani, H. M., Sayadi, H., Nazari, N., Khasawneh, K. N., Sasan, A., Rafatirad, S., & Homayoun, H. (2021) · International Symposium on Secure and Private Execution Environment Design (SEED) · doi:10.1109/seed51797.2021.00011 · Cited by 15

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

Robust Online Learning against Malicious Manipulation with Application to Network Flow Classification

Li, Y., Liang, B., & Tizghadam, A. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488890 · Cited by 14

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

Decision Boundary-aware Data Augmentation for Adversarial Training

Chen, C., Zhang, J., Xu, X., Lyu, L., Chen, C., Hu, T., & Chen, G. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3165889 · Cited by 15

Adversarial supervised contrastive learning

Li, Z., Yu, D., Wu, M., Jin, C., & Yu, H. (2022) · Machine Learning · doi:10.1007/s10994-022-06269-7 · Cited by 14

Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation

Taran, O., Rezaeifar, S., Holotyak, T., & Voloshynovskiy, S. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00106-x · Cited by 15

Certifying Decision Trees Against Evasion Attacks by Program Analysis

Calzavara, S., Ferrara, P., & Lucchese, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59013-0_21 · Cited by 14

Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks

Mirzaeian, A., Košecká, J., Homayoun, H., Mohsenin, T., & Sasan, A. (2021) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed51717.2021.9424353 · Cited by 14

From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories

Kazmi, S. M. K. A., Aafaq, N., Khan, M. A., Khalil, M., & Saleem, A. (2023) · IEEE Access · doi:10.1109/access.2023.3299878 · Cited by 13

An Introduction to Adversarially Robust Deep Learning

Peck, J., Goossens, B., & Saeys, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331087 · Cited by 16

ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks

Clifford, E. R., Shumailov, I., Zhao, Y., Anderson, R., & Mullins, R. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00024 · Cited by 13

Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models

Ma, T., Jia, X., Duan, R., Li, X. Q., Huang, Y., Jia, X., Chu, Z., & Ren, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00258 · Cited by 40

Poster

Labaca-Castro, R., Biggio, B., & Rodosek, G. D. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363257 · Cited by 13

Causative label flip attack detection with data complexity measures

Chan, P. P. K., He, Z., Hu, X., Tsang, E. C. C., Yeung, D., & Ng, W. W. Y. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01159-7 · Cited by 13

EvadeRL: Evading PDF Malware Classifiers with Deep Reinforcement Learning

Mao, Z., Fang, Z., Li, M., & Fan, Y. (2022) · Security and Communication Networks · doi:10.1155/2022/7218800 · Cited by 13

Classification and Analysis of Adversarial Machine Learning Attacks in IoT: a Label Flipping Attack Case Study

Abrishami, M., Dadkhah, S., Neto, E. C. P., Xiong, P., Iqbal, S., Ray, S., & Ghorbani, A. A. (2022) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct56874.2022.9953823 · Cited by 13

Adversarially Robust Continual Learning

Khan, H., Bouaynaya, N. C., & Rasool, G. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892970 · Cited by 13

On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

Dyrmishi, S., Ghamizi, S., Simonetto, T., Traon, Y. L., & Cordy, M. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179316 · Cited by 13

Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

Coletta, A., Jerome, J., Savani, R., & Vyetrenko, S. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626854 · Cited by 13

ViTH-RFG: Vision Transformer Hashing With Residual Fuzzy Generation for Targeted Attack in Medical Image Retrieval

Ding, W., Liu, C., Huang, J., Cheng, C., & Ju, H. (2023) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2023.3343352 · Cited by 13

Adversarial Attacks and Defenses in Malware Detection Classifiers

John, T. S., & Thomas, T. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-8407-0.ch007 · Cited by 13

Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks

McCarthy, A., Andriotis, P., Ghadafi, E., & Legg, P. (2021) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa52016.2021.9478199 · Cited by 13

Threat is in the Air

Pajola, L., Pasa, L., & Conti, M. (2019) · ACM Workshop on Wireless Security and Machine Learning (WiseML) · doi:10.1145/3324921.3328783 · Cited by 13

Stealthy 3D Poisoning Attack on Video Recognition Models

Xie, S., Yan, Y., & Hong, Y. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3163397 · Cited by 12

Pixel Bleach Network for Detecting Face Forgery Under Compression

Li, C., Zheng, Z., Bin, Y., Wang, G., Yang, Y., Li, X., & Shen, H. T. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3301242 · Cited by 12

BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers: A Comprehensive Study

Li, X., Miller, D. J., Xiang, Z. J., & Kesidis, G. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3365548 · Cited by 12

Practical Attacks Against DNS Reputation Systems

Galloway, T., Karakolios, K., Ma, Z., Perdisci, R., Keromytis, A. D., & Antonakakis, M. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00266 · Cited by 12

Improving the security of image manipulation detection through one-and-a-half-class multiple classification

Barni, M., Nowroozi, E., & Tondi, B. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08425-z · Cited by 12

Optimization and deployment of CNNs at the edge

Meloni, P., Loi, D., Busia, P., Deriu, G., Pimentel, A. D., Sapra, D., Stefanov, T. P., Minakova, S., Conti, F., Benini, L., et al. (2019) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3310273.3323435 · Cited by 13

AuxBlocks: Defense Adversarial Examples via Auxiliary Blocks

Yu, Y., Yu, P., & Li, W. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851823 · Cited by 12

Adversarial Fooling Beyond "Flipping the Label"

Mopuri, K. R., Shaj, V., & Babu, R. V. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00397 · Cited by 12

Marble

Du, X., Li, Y., Xie, X., Ma, L., Liu, Y., & Zhao, J. (2020) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3324884.3416564 · Cited by 15

1-Bit Radar Imaging Based on Adversarial Samples

Han, J., Li, G., Wang, K., Duan, M., & Zhang, X.-P. S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3073159 · Cited by 12

Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes

Billah, M., Anwar, A., Rahman, Z., & Galib, S. M. (2021) · Energies · doi:10.3390/en14133887 · Cited by 12

FooBaR: Fault Fooling Backdoor Attack on Neural Network Training

Breier, J., Hou, X., Ochoa, M., & Solano, J. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3166671 · Cited by 13

Evaluation of adversarial machine learning tools for securing AI systems

Asha, S., & Vinod, P. (2021) · Cluster Computing · doi:10.1007/s10586-021-03421-1 · Cited by 13

Cyberattacks Against Critical Infrastructure Facilities and Corresponding Countermeasures

Vähäkainu, P., Lehto, M., & Kariluoto, A. (2022) · Computational methods in applied sciences · doi:10.1007/978-3-030-91293-2_11 · Cited by 12

Randomized Moving Target Approach for MAC-Layer Spoofing Detection and Prevention in IoT Systems

Madani, P., Vlajic, N., & Maljević, I. (2022) · Digital Threats Research and Practice · doi:10.1145/3477403 · Cited by 12

Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

Schneider, J., & Apruzzese, G. (2022) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw54247.2022.9833874 · Cited by 12

Exploring Adversarially Robust Training for Unsupervised Domain Adaptation

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Toward Face Biometric De-identification using Adversarial Examples

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Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts

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Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks

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Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference

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A Deeper Analysis of Adversarial Examples in Intrusion Detection

Merzouk, M. A., Cuppens, F., Cuppens-Boulahia, N., & Yaich, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68887-5_4 · Cited by 12

Decamouflage: A Framework to Detect Image-Scaling Attacks on CNN

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Fairness & friends in the data science era

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Learning a Secure Classifier against Evasion Attack

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

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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

Secure Kernel Machines against Evasion Attacks

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

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