Adversarial Edit Attacks for Tree Data

Paaßen B (Accepted)
In: Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Yin H, Camacho D, Tino P (Eds); .

Konferenzbeitrag | Angenommen | Englisch
 
Autor/in
Herausgeber*in
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Abstract / Bemerkung
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.
Stichworte
Adversarial attacks; Tree edit distance; Structured data; Tree kernels; Recursive neural networks; Tree echo state networks
Erscheinungsjahr
2019
Titel des Konferenzbandes
Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019)
Konferenz
20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019)
Konferenzort
Manchester, UK
Konferenzdatum
2019-11-14 – 2019-11-16
Page URI
https://pub.uni-bielefeld.de/record/2937053

Zitieren

Paaßen B. Adversarial Edit Attacks for Tree Data. In: Yin H, Camacho D, Tino P, eds. Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Accepted.
Paaßen, B. (Accepted). Adversarial Edit Attacks for Tree Data. In H. Yin, D. Camacho, & P. Tino (Eds.), Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019)
Paaßen, B. (Accepted). “Adversarial Edit Attacks for Tree Data” in Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019), Yin, H., Camacho, D., and Tino, P. eds.
Paaßen, B., Accepted. Adversarial Edit Attacks for Tree Data. In H. Yin, D. Camacho, & P. Tino, eds. Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019).
B. Paaßen, “Adversarial Edit Attacks for Tree Data”, Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019), H. Yin, D. Camacho, and P. Tino, eds., Accepted.
Paaßen, B.: Adversarial Edit Attacks for Tree Data. In: Yin, H., Camacho, D., and Tino, P. (eds.) Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). (Accepted).
Paaßen, Benjamin. “Adversarial Edit Attacks for Tree Data”. Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2019). Ed. Hujun Yin, David Camacho, and Peter Tino. Accepted.
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OA Open Access
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Beschreibung
Software for edit distance computations

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arXiv: 1908.09364

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