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Evasion Attacks against Machine Learning at Test Time
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“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]
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“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]
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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)
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Liu, Z., Jia, Z. Y., & Lu, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34637-9_6 · Cited by 2
Filtering Adversarial Noise with Double Quantization
MaungMaung, A., Kinoshita, Y., & Kiya, H. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023341 · Cited by 2
Convolutional Neural Network Visualization in Adversarial Example Attack
Yu, C., Wang, X., & Li, Y. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-7981-3_16 · Cited by 2
Lower Bounds for Adversarially Robust PAC Learning under Evasion and Hybrid Attacks
Diochnos, D. I., Mahloujifar, S., & Mahmoody, M. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00117 · Cited by 2
AMEBA: An Adaptive Approach to the Black-Box Evasion of Machine Learning Models
Calzavara, S., Cazzaro, L., & Lucchese, C. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3453114 · Cited by 2
Assessing adversarial training effect on IDSs and GANs
Chaitou, H., Robert, T., Leneutre, J., & Pautet, L. (2021) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr51186.2021.9527949 · Cited by 2
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
Meloni, E., Tiezzi, M., Pasqualini, L., Gori, M., & Melacci, S. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00009 · Cited by 3
Keppel, J., Liebers, J., Auda, J., Gruenefeld, U., & Schneegaß, S. (2022) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3568444.3568469 · Cited by 2
Dissecting Adversarial Robustness of Multimodal LM Agents
Wu, C. H., Shah, R., Koh, J. Y., Salakhutdinov, R., Fried, D., & Raghunathan, A. (2024) · arXiv · doi:10.48550/arxiv.2406.12814 · Cited by 1
Evolving Architectures With Gradient Misalignment Toward Low Adversarial Transferability
Operiano, K. R. G., Pora, W., Iba, H., & Kera, H. (2021) · IEEE Access · doi:10.1109/access.2021.3134840 · Cited by 1
Integrative System of Deep Classifiers Certification: Case of Convolutional Attacks
Smati, I., Khalsi, R., Mziou-Sallami, M., Adjed, F., & Ghorbel, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22953-4_5 · Cited by 1
Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags
Imam, N. H., & Vassilakis, V. G. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3243-6_32 · Cited by 1
Minimum Selection Feature Importance Guided Attack
Karumanchi, R., & Gressel, G. (2023) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic57670.2023.10263830 · Cited by 1
OMG-Attack: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks
Tal, O. B., Haviv, A., & Bermano, A. H. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00397 · Cited by 1
Adversarial Robustness on Image Classification With k-Means
Omari, R. M., Kim, J., & Montague, P. (2024) · IEEE Access · doi:10.1109/access.2024.3365517 · Cited by 1
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.
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