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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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The Limitations of Deep Learning in Adversarial Settings
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Universal Adversarial Perturbations
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., & Bengio, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01236 · Cited by 2,325
Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., & Song, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00175 · Cited by 2,173
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Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
Improving Transferability of Adversarial Examples With Input Diversity
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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., & Bethge, M. (2017) · arXiv · doi:10.48550/arxiv.1712.04248 · Cited by 1,018
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., & Zhu, J. (2018) · 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.2018.00191 · Cited by 987
Safety Verification of Deep Neural Networks
Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63387-9_1 · Cited by 903
Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
Thys, S., Ranst, W. V., & Goedemé, T. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00012 · Cited by 744
Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., & Goldstein, T. (2019) · ETH Zurich · doi:10.3929/ethz-b-000460345 · Cited by 580
Data collection and quality challenges in deep learning: a data-centric AI perspective
Whang, S. E., Roh, Y., Song, H., & Lee, J.-G. (2023) · The VLDB Journal · doi:10.1007/s00778-022-00775-9 · Cited by 573
Adversarial Examples for Malware Detection
Grosse, K., Papernot, N., Manoharan, P., Michael Backes, A., & McDaniel, P. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66399-9_4 · Cited by 584
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
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Self-citationPrivacy and Robustness in Federated Learning: Attacks and Defenses
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Data-centric Artificial Intelligence: A Survey
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SoK: Security and Privacy in Machine Learning
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DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
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Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421
Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019) · IEEE Access · doi:10.1109/access.2019.2942390 · Cited by 442
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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 ›
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