The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Evasion Attacks against Machine Learning at Test Time
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They demonstrated that DBA is substantially more persistent and stealthy on diverse datasets such as finance and image data.”
From Vulnerabilities in federated learning (2021) · cites this paper as [44]
In the citing paper
“However, only a few works [44] investigate the poisoning attacks in a distributed environment where multiple malicious clients with the same attack strive to include poisoned training samples into the training procedures.”
From Vulnerabilities in federated learning (2021) · cites this paper as [44]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Self-citationDalvi, 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
Self-citationLowd, 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
Self-citationNightmare 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
Self-citationLearning 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
Research works printed but not linked: 11
- Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: Langford, J., Pineau, J. (eds.) 29th Int’l Conf. on Mach. Learn. (2012)
- Brückner, M., Kanzow, C., Scheffer, T.: Static prediction games for adversarial learning problems. J. Mach. Learn. Res. 13, 2617–2654 (2012)
- Fogla, P., Sharif, M., Perdisci, R., Kolesnikov, O., Lee, W.: Polymorphic blending attacks. In: Proc. 15th Conf. on USENIX Sec. Symp. USENIX Association, CA (2006)
- Kloft, M., Laskov, P.: Online anomaly detection under adversarial impact. In: Proc. of the 13th Int’l Conf. on Art. Int. and Stats (AISTATS), pp. 405–412 (2010)
- Kolcz, A., Teo, C.H.: Feature weighting for improved classifier robustness. In: Sixth Conf. on Email and Anti-Spam (CEAS), Mountain View, CA, USA (2009)
- Laskov, P., Kloft, M.: A framework for quantitative security analysis of machine learning. In: AISec 2009: Proc. of the 2nd ACM Works. on Sec. and Art. Int., pp. 1–4. ACM, New York (2009)
- LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Müller, U., Säckinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: Int’l Conf. on Art. Neu. Net., pp. 53–60 (1995)
- Nelson, B., Barreno, M., Chi, F.J., Joseph, A.D., Rubinstein, B.I.P., Saini, U., Sutton, C., Tygar, J.D., Xia, K.: Exploiting machine learning to subvert your spam filter. In: LEET 2008: Proc. of the 1st USENIX Work. on L.-S. Exp. and Emerg. Threats, pp. 1–9. USENIX Association, Berkeley (2008)
- Nelson, B., Rubinstein, B.I., Huang, L., Joseph, A.D., Lee, S.J., Rao, S., Tygar, J.D.: Query strategies for evading convex-inducing classifiers. J. Mach. Learn. Res. 13, 1293–1332 (2012)
- Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Adv. in L. M. Class, pp. 61–74 (2000)
- Šrndić, N., Laskov, P.: Detection of malicious pdf files based on hierarchical document structure. In: Proc. 20th Annual Net. & Dist. Sys. Sec. Symp. (2013)
Cited by
Sorted by citations
Items 701–800 of 904
Towards Video-Text Retrieval Adversarial Attack
Yang, H., Xiang, Y., Sun, K., Hu, J., & Lin, X. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448358 · Cited by 1
Short Review on Supervised Learning Under Adversarial Label Poisoning and Clean-Label Data Poisoning
Tavllali, P., & Behzadan, V. (2023) · Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE) · doi:10.1109/csce60160.2023.00067 · Cited by 3
Pujari, M., & Sun, W. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3289-0_52 · Cited by 1
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis
Franco, N., Kempkes, M., Spiegelberg, J., & Lorenz, J. M. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00042 · Cited by 1
Hu, H., & Pang, J. (2025) · Applied Intelligence · doi:10.1007/s10489-024-05974-1 · Cited by 1
Some Cybersecurity Issues in Artifical Intelligence Systems
Sgurev, V., & Doukovska, L. (2024) · International Conference Automatics and Informatics (ICAI) · doi:10.1109/icai63388.2024.10851683 · Cited by 1
He, F., & Tao, D. (2025) · Machine learning · doi:10.1007/978-981-16-8233-9_13 · Cited by 1
PixelDenoise: Purifying Adversarial Images in a Black-Box Way
Zhu, T., Sun, H., & Fan, Y. (2024) · International Conference on Electronic Information Engineering and Computer Technology (EIECT) · doi:10.1109/eiect64462.2024.10866183 · Cited by 1
LFPD: Local-Feature-Powered Defense Against Adaptive Backdoor Attacks
Guo, W., Demontis, A., Pintor, M., Chan, P. P. K., & Biggio, B. (2024) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc63072.2024.10935153 · Cited by 1
Self-citationLiu, S., Zhang, Y., & Zhao, Y. (2025) · Electronics · doi:10.3390/electronics14102020 · Cited by 1
Precise Language Deception: XAI Driven Targeted Adversarial Examples with Restricted Knowledge
Gniewkowski, M., Walkowiak, P., Klonowski, M., & Walkowiak, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97626-1_4 · Cited by 1
Composite Explanation-Aware Attacks
Noppel, M., & Wressnegger, C. (2025) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw67851.2025.00022 · Cited by 1
Enhancing Privacy in Smart Grids and IOTs Systems by Using Federated Learning: Case Study
Ali, A., Drlík, M., Wadi, M., & Elmasry, W. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106876 · Cited by 1
On the Robustness of GUI Grounding Models Against Image Attacks
Zhao, H., Chen, T., & Wang, Z. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00149 · Cited by 1
Position-Aware Stamp-Like Adversarial Attack for Document Classification
Dong, Q., Kang, L., Pintor, M., & Karatzas, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04627-7_17 · Cited by 1
Data-Centric Robust Training for Defending Against Transfer-Based Adversarial Attacks
Yang, Y., Cao, R., Ji, X., Tian, Q., Lin, C., Zhao, Z., Li, Q., Yang, L., Yang, H., & Shen, C. Q. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3611148 · Cited by 1
GraphZOOM: subgraph black-box attack against inductive graph neural networks
Tang, R., Moshili, A., Huang, K., Mao, J., & Lin, J. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02129-8 · Cited by 1
GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search
Ben-Tov, M., & Sharif, M. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3765095 · Cited by 1
Khemis, S., Amara, Y., Benatia, M. A., Messalti, I., Khanous, M. E. I., & Bernard De Baets, C. (2025) · Machine Learning · doi:10.1007/s10994-025-06833-x · Cited by 1
Privacy-Preserving Android Malware Detection Using Deep Federated Learning
Coulibaly, R. B., Bissyandé, T. F., Kafando, R., Sabané, A., & Kaboré, A. K. (2025) · Cybersecurity4D (C4D) · doi:10.1109/c4d65382.2025.11306458 · Cited by 1
Identifying unpredictable test examples with worst-case guarantees
Goldwasser, S., Kalai, Y. T., Kalai, Y. T., & Montasser, O. (2020) · Information Theory and Applications Workshop (ITA) · doi:10.1109/ita50056.2020.9244996 · Cited by 1
A Neuro-Inspired Autoencoding Defense Against Adversarial Attacks
Bakiskan, C., Cekic, M., Sezer, A. D., & Madhow, U. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506184 · Cited by 1
Negative Adversarial Example Generation Against Naver's Celebrity Recognition API
Kim, K., & Woo, S. S. (2022) · doi:10.1145/3494109.3527193 · Cited by 1
Applying Machine Learning to use security oracles: a case study in virus and malware detection
Preuveneers, D., Lavens, E., & Joosen, W. (2022) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw55150.2022.00030 · Cited by 1
Publishing Efficient On-device Models Increases Adversarial Vulnerability
Hong, S., Carlini, N., & Kurakin, A. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00026 · Cited by 2
ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems
Hussain, S., Huster, T. P., Mesterharm, C., Neekhara, P., & Koushanfar, F. (2023) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58367.2023.00038 · Cited by 1
Boosting Adversarial Attacks with Improved Sign Method
Guo, B., Yang, Y., Li, Q., Hou, J., & Rao, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_11 · Cited by 1
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Piras, G., Pintor, M., Demontis, A., & Biggio, B. (2023) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc58545.2023.10327927 · Cited by 1
Self-citationAdversarial Examples with Specular Highlights
Vats, V., & Jerripothula, K. R. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00388 · Cited by 1
Data Poisoning Attacks in Cognitive Computing
Koundinya, A. K., Patil, S. S., & Chandu, B. R. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10544345 · Cited by 1
An Overview of Privacy-Preserving and Security Methods for Deep Learning
Goel, P. K., Verma, S., Mahur, L. S., Aeron, A., Jayant, C., & Komal, K. (2024) · International Conference on Innovations and Challenges in Emerging Technologies (ICICET) · doi:10.1109/icicet59348.2024.10616340 · Cited by 1
PE-Attack: On the Universal Positional Embedding Vulnerability in Transformer-Based Models
Gao, S., Zhou, H., Chen, T., He, M., Xu, R., & Li, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3442617 · Cited by 1
It’s Simplex! Disaggregating Measures to Improve Certified Robustness
Cullen, A. C., Montague, P., Liu, S., Erfani, S., & Rubinstein, B. I. P. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00065 · Cited by 1
AutoML in the Face of Adversity: Securing Mobility Predictions in NWDAF
Atiiq, S. A., Gehrmann, C., Yuan, Y., & Sternby, J. (2024) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec62297.2024.10710314 · Cited by 1
Label Noise Resistant f-Divergence Receiver for Power Line Communications
Novello, N., & Tonello, A. M. (2024) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm60555.2024.10738036 · Cited by 1
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization
Cheng, Z., Zhu, F., Zhang, X.-Y., & Liu, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78395-1_28 · Cited by 1
Evaluating Model Robustness Using Adaptive Sparse L0 Regularization
Liu, W., Li, Z., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0814-0_1 · Cited by 1
Detectable Mislabeling – Can Faulty AI Models be Recognized from Incomplete Memory Traces?
Krzywiecki, Ł., Kulczycki, T., Nteranya, C. E., & Stós, A. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00329 · Cited by 1
Mbow, M., Román, R., Seal, A., Wang, K. I., Mohanty, S. K., & Sakurai, K. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1624-4_4 · Cited by 1
Cost-effective and robust adversarial patch attacks in real-world scenarios
Tiliwalidi, K., Yan, K., Shi, Y., Hu, C., & Zhou, J. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.3.033003 · Cited by 1
Sharma, D. P., Lashkari, A. H., Firoozjaei, M. D., Mahdavifar, S., & Xiong, P. (2025) · Progress in IS · doi:10.1007/978-3-031-91524-6_8 · Cited by 1
Verifiable Boosted Tree Ensembles
Calzavara, S., Cazzaro, L., Lucchese, C., & Pibiri, G. E. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00022 · Cited by 1
Wang, S., Li, W., Xu, Z., & Yu, N. (2025) · International Conference on Electronic Engineering and Information Systems (EEISS) · doi:10.1109/eeiss65394.2025.11085508 · Cited by 1
Adversarial Robustness of Machine Learning-Based Access Control
Llamas, J. M., Hoof, M. V., Preuveneers, D., & Joosen, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00630-1_6 · Cited by 1
Mançellari, E., Topal, A. O., & Leprévost, F. (2025) · ACI Avances en Ciencias e Ingenierías · doi:10.18272/aci.vi.3699 · Cited by 1
A Notion of Uniqueness for the Adversarial Bayes Classifier
Frank, N. S. (2026) · SIAM Journal on Mathematics of Data Science · doi:10.1137/24m1664150 · Cited by 1
Towards Building Active Defense Systems for Software Applications
Perumal, Z., & Veeramachaneni, K. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94147-9_12 · Cited by 1
Delving into Diversity in Substitute Ensembles and Transferability of Adversarial Examples
Hang, J., Han, K., & Li, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04182-3_16 · Cited by 1
Distinguishability of adversarial examples
Qin, Y., Hunt, R., & Yue, C. (2020) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3407023.3407040 · Cited by 1
An MCTS-based Adversarial Training Method for Image Recognition
Liu, Y., & Lomuscio, A. R. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852337 · Cited by 1
Evasion Attack for Fingerprint Biometric System and Countermeasure
Lakshmi, S. M., Kaur, M., Shukla, A. K., & Pathania, N. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0324-5_7 · Cited by 1
Liu, X.-Y., Bai, Y., Xia, S., & Jiang, Y. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053237 · Cited by 1
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.002 · Cited by 1
Self-citationReliability on Deep Learning Models: A Comprehensive Observation
Zhang, Y., & Xiao, C. (2020) · International Symposium on System and Software Reliability (ISSSR) · doi:10.1109/isssr51244.2020.00034 · Cited by 1
Feature-Based Adversarial Attacks Against Machine Learnt Mobile Malware Detectors
Shahpasand, M., Hamey, L., Kâafar, M. A., & Vatsalan, D. (2020) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac50341.2020.9315144 · Cited by 1
A game-theoretic approach to adversarial linear Gaussian classification
Farokhi, F. (2021) · IFAC Journal of Systems and Control · doi:10.1016/j.ifacsc.2021.100163 · Cited by 1
Layer-Wise Activation Cluster Analysis of CNNs to Detect Out-of-Distribution Samples
Lehmann, D., & Ebner, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86380-7_18 · Cited by 1
Towards Transferable 3D Adversarial Attack
Lu, Q., Wei, S., Chu, H., & Zhao, Y. (2021) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3469877.3493596 · Cited by 1
Mitigating adversarial perturbations via weakly supervised object location and regions recombination
Wu, F., Guo, T., Zhang, J., & Xiao, L. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01281-2 · Cited by 1
Security and Robustness in Federated Learning
Rawat, A., Zizzo, G., Hameed, M. Z., & Muñoz-González, L. (2022) · Springer eBooks · doi:10.1007/978-3-030-96896-0_16 · Cited by 1
Attacking Random Forest Classifiers based on Shortest Path Algorithm
Wang, T., & Zhang, F. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00039 · Cited by 1
Detection of Geometry-based Adversarial Facial Attacks using Error Level Analysis
Sadu, C., & Das, P. K. (2022) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon55691.2022.9978132 · Cited by 1
An Exploration of Poisoning Attacks on Data-Based Decision Making
Kinsey, S., Wong, W. T., Sinha, A., & Nguyen, T. H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26369-9_12 · Cited by 1
Misleading Image Classification with Multi-shearing and Random Padding
Li, C., Li, Z., Yang, B., & Zhang, H. (2022) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3573428.3573661 · Cited by 1
Analysis of Adversarial Attacks on Support Vector Machine
Dakhale, B., Vipinkumar, K., Narotham, K., Pungati, S. J., Bhurane, A. A., & Kothari, A. (2023) · International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS) · doi:10.1109/pcems58491.2023.10136124 · Cited by 1
Human intuition as a defense against attribute inference
Waniek, M., Suri, N., Zameek, A., AlShebli, B., & Rahwan, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-43062-5 · Cited by 1
Accelerated Adversarial Attack Generation and Enhanced Decision Insight
Kumarasiri, N. K. Y. S., Premaratne, S. C., & Wijesuriya, W. M. R. M. (2023) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr61062.2023.10382716 · Cited by 1
Accessorize in the Dark: A Security Analysis of Near-Infrared Face Recognition
Cohen, A., & Sharif, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51479-1_3 · Cited by 1
Online Adaptive Mahalanobis Distance Estimation
Qin, L., Reddy, A. R. M., & Song, Z. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386869 · Cited by 1
Android Malware Detection using HexCode FeaturesPreprint
Anand, A., Singh, J. P., & Dhoundiyal, V. (2024) · Research Square · doi:10.21203/rs.3.rs-4544871/v1 · Cited by 1
Ctrl-Z: Controlling AI Agents via Resampling
Bhatt, A., Rushing, C., Kaufman, A., Tracy, T., Georgiev, V., Matolcsi, D., Khan, A., & Shlegeris, B. (2025) · arXiv · doi:10.48550/arxiv.2504.10374
“Adversarial robustness to a variety of inputs has been extensively studied across a variety of models (Biggio et al., 2013; Papernot et al., 2016), including Large Language Models (Zou et al., 2023).”
Too Big to FAIL: What You Need to Know Before Attacking a Machine Learning System
Dumitraş, T., Kaya, Y., Mărginean, R., & Suciu, O. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03251-7_17
A retrieval-based approach for diverse and image-specific adversary selection
Ravat, R. S., & Verma, Y. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00177-8
Adversarial Machine Learning Challenges
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.009
Self-citationSubramanya, A., Mopuri, K. R., & Babu, R. V. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293387
A Framework for Secure Learning
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.003
Self-citationWhere Does the Robustness Come from?
Liao, C., Cheng, Y., Fang, C., & Shi, J. (2020) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3411508.3421380
On Recent Security Issues in Machine Learning
Alani, M. M. (2020) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom50211.2020.9238337
Adversarial Attacks in a Deep Reinforcement Learning based Cluster Scheduler
Zhang, S.-J., Wang, C., & Zomaya, A. Y. (2020) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots50786.2020.9285955
ECEM - Generating Adversarial Logs under Black-box Setting in Web Security
Fei, Y., Wang, L., He, R., & Lu, J. (2020) · Globecom · doi:10.1109/globecom42002.2020.9347996
ConTheModel: Can We Modify Tweets to Confuse Classifier Models?
Vinay, A. R., Alawami, M. A., & Kim, H. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-72725-3_15
Kernel Optimization in SVM for Defense Against Adversarial Attacks
Li, W., & Liu, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73671-2_4
Integrity Attack Case Study: PCA Detector
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.006
Self-citationTowards Developing Better Object Detectors for Real-World Use
Gupta, A., & Srivastava, B. (2022) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3493700.3493741
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.001
Self-citationJoseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.015
Self-citationAppendix D: Full Proofs for Near-Optimal Evasion
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.013
Self-citationAppendix A: Background for Learning and Hyper-Geometry
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.010
Self-citationAttacking a Hypersphere Learner
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.004
Self-citationPrivacy-Preserving Mechanisms for SVM Learning
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.007
Self-citationAvailability Attack Case Study: SpamBayes
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.005
Self-citationAppendix B: Full Proofs for Hypersphere Attacks
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.011
Self-citationNear-Optimal Evasion of Classifiers
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.008
Self-citationAppendix C: Analysis of SpamBayes
Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.012
Self-citationJoseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.014
Self-citationJoseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.016
Self-citationAdversarial Examples and Defenses
Maung, A. P. M., Kinoshita, Y., & Kiya, H. (2022) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-19-1524-6_2
Improving Adversarial Robustness of Deep Neural Networks via Linear Programming
Tang, X., Yang, Z., Fu, X., Wang, J., & Zeng, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10363-6_22
Adversarial Defense with Secret Key
Maung, A. P. M., & Kiya, H. (2022) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-19-1524-6_7
Towards Interpreting Vulnerability of Object Detection Models via Adversarial Distillation
Zhang, Y., Tan, Y., Lu, M.-F., Liu, L., Zhang, Q., Li, Y., & Wang, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16815-4_4
Metadata
- Authors
- Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F.
- Affiliations
- University of Cagliari (IT)
- University of Potsdam (DE)
- University of Tübingen (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 387-402
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3103836116
- Cited by
- 890
- References
- 13
- Citations by year
- 2026: 35 · 2025: 103 · 2024: 131 · 2023: 123 · 2022: 126 · 2021: 95 · 2020: 98 · 2019: 98 · 2018: 37 · 2017: 26 · 2016: 7 · 2015: 5 · 2014: 4 · 2013: 1
- FWCI
- 3.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Keywords
- evasion attacks, adversarial learning, gradient-based attacks, malware detection, PDF documents, countermeasures
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
