The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Efficient Defenses Against Adversarial Attacks
Open accessAbstract
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.
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Cites
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Towards Evaluating the Robustness of Neural Networks
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Adversarial examples in the physical world
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Practical Black-Box Attacks against Machine Learning
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Cited by
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., & Wagner, D. (2018) · arXiv · doi:10.48550/arxiv.1802.00420 · Cited by 2,172
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021
Adversarial Attacks and Defenses in Deep Learning
Ren, K., Zheng, T., Qin, Z., & Liu, X. (2020) · Engineering · doi:10.1016/j.eng.2019.12.012 · Cited by 678
Adversarial Robustness Toolbox v1.0.0
Nicolae, M.-I., Sinn, M., Tran, M. N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., et al. (2018) · arXiv · doi:10.48550/arxiv.1807.01069 · Cited by 594
Self-citationTowards Robust Neural Networks via Random Self-ensemble
Liu, X., Cheng, M., Zhang, H., & Hsieh, C.-J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_23 · Cited by 386
Metadata
- Authors
- Zantedeschi, V., Nicolae, M.-I., & Rawat, A.
- Venue
- Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security
- Publisher
- ACM
- Published
- 2017-11-03
- Pages
- 39-49
- OpenAlex
- W2738001131
- Cited by
- 312
- References
- 29
- FWCI
- 16.89
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zantedeschi, V., Nicolae, M.-I., & Rawat, A. (2017). Efficient Defenses Against Adversarial Attacks. Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. https://doi.org/10.1145/3128572.3140449
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