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Adversarial Edit Attacks for Tree Data
- Source :
- Intelligent Data Engineering and Automated Learning – IDEAL 2019 ISBN: 9783030336066, IDEAL (1)
- Publication Year :
- 2019
- Publisher :
- Springer International Publishing, 2019.
-
Abstract
- Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research on adversarial attacks to date is limited to vectorial data, in particular image data. In this contribution, we extend the field by introducing adversarial edit attacks for tree-structured data with potential applications in medicine and automated program analysis. Our approach solely relies on the tree edit distance and a logarithmic number of black-box queries to the attacked classifier without any need for gradient information. We evaluate our approach on two programming and two biomedical data sets and show that many established tree classifiers, like tree-kernel-SVMs and recursive neural networks, can be attacked effectively.<br />accepted at the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
050101 languages & linguistics
Tree edit distance
Logarithm
Computer science
Machine Learning (stat.ML)
02 engineering and technology
Machine learning
computer.software_genre
Machine Learning (cs.LG)
Adversarial system
Program analysis
Statistics - Machine Learning
0202 electrical engineering, electronic engineering, information engineering
Recursive neural networks
0501 psychology and cognitive sciences
Tree kernels
Structured data
business.industry
Adversarial attacks
05 social sciences
Tree echo state networks
020201 artificial intelligence & image processing
Artificial intelligence
Tree kernel
business
computer
Classifier (UML)
Subjects
Details
- ISBN :
- 978-3-030-33606-6
- ISBNs :
- 9783030336066
- Database :
- OpenAIRE
- Journal :
- Intelligent Data Engineering and Automated Learning – IDEAL 2019 ISBN: 9783030336066, IDEAL (1)
- Accession number :
- edsair.doi.dedup.....99d4baa48a0b7745253af66320923a0d