The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Open accessAbstract
Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
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“aw inputs without any human guidance besides labeled training data. In recent years, DNNs have surpassed human performance in many application domains due to increasing availability of large datasets [20, 38, 47], specialized hardware [34, 50], and efficient training algorithms [31, 39, 66, 71]. A DNN consists of mult…”
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Adversarial Examples: Attacks and Defenses for Deep Learning
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Machine Learning Testing: Survey, Landscapes and Horizons
Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897
Certified Robustness to Adversarial Examples with Differential Privacy
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Self-citationA Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
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Metadata
- Authors
- Pei, K., Cao, Y., Yang, J., & Jana, S.
- Venue
- Proceedings of the 26th Symposium on Operating Systems Principles
- Publisher
- ACM
- Published
- 2017-10-14
- Pages
- 1-18
- OpenAlex
- W2616028256
- Cited by
- 1,217
- References
- 22
- FWCI
- 89.20
- Type
- conference paper
- Language
- en
- Open access
- green
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- Pei, K., Cao, Y., Yang, J., & Jana, S. (2017). DeepXplore: Automated Whitebox Testing of Deep Learning Systems. Proceedings of the 26th Symposium on Operating Systems Principles. https://doi.org/10.1145/3132747.3132785
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