The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Wild patterns: Ten years after the rise of adversarial machine learning
Open accessAbstract
Deep neural networks and machine-learning algorithms are pervasively used in several applications, ranging from computer vision to computer security. In most of these applications, the learning algorithm has to face intelligent and adaptive attackers who can carefully manipulate data to purposely subvert the learning process. As these algorithms have not been originally designed under such premises, they have been shown to be vulnerable to well-crafted, sophisticated attacks, including training-time poisoning and test-time evasion attacks (also known as adversarial examples). The problem of countering these threats and learning secure classifiers in adversarial settings has thus become the subject of an emerging, relevant research field known as adversarial machine learning. The purposes of this tutorial are: (a) to introduce the fundamentals of adversarial machine learning to the security community; (b) to illustrate the design cycle of a learning-based pattern recognition system for adversarial tasks; (c) to present novel techniques that have been recently proposed to assess performance of pattern classifiers and deep learning algorithms under attack, evaluate their vulnerabilities, and implement defense strategies that make learning algorithms more robust to attacks; and (d) to show some applications of adversarial machine learning to pattern recognition tasks like object recognition in images, biometric identity recognition, spam and malware detection.
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Citations
Cites
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Towards Evaluating the Robustness of Neural Networks
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
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The Limitations of Deep Learning in Adversarial Settings
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Cited by
Adversarial attacks on medical machine learning
Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019) · Science · doi:10.1126/science.aaw4399 · Cited by 1,088
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861
Self-citationDo ImageNet Classifiers Generalize to ImageNet?
Recht, B., Roelofs, R., Schmidt, L., & Shankar, V. (2019) · arXiv · doi:10.48550/arxiv.1902.10811 · Cited by 790
Challenges in deploying machine learning: A survey of case studies
Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638
Metadata
- Authors
- Biggio, B., & Roli, F.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 84
- Pages
- 317-331
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2773446523
- Cited by
- 1,409
- References
- 23
- FWCI
- 86.99
- Type
- journal article
- Language
- en
- Open access
- green
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