DeepXplore: automated whitebox testing of deep learning systems

Abstract

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains such as 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.

We design, implement, and evaluate DeepXplore, the first white-box framework for systematically testing real-world DL systems. First, we introduce neuron coverage for measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.

DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets such as ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.

References

1. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604,07316 (2016).
2. Brubaker, C., Jana, S., Ray, B., Khurshid, S., Shmatikov V. Using frankencerts for automated adversarial testing of certificate validation in SSL/TLS implementations. In Proceedings of the 35th IEEE Symposium on Security and Privacy (2014).
3. Goodfellow, I., Shlens, J., Szegedy, C. Explaining and harnessing adversarial examples. In Proceedings of the 3rd International Conference on Learning Representations (2015).
4. Grosse, K., Papernot, N., Manoharan, P., Backes, M., McDaniel, P. Adversarial examples for malware detection. In European Symposium on Research in Computer Security (2017).
5. He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016).
6. Julian, K.D., Lopez, J., Brush, J.S., Owen, M.P., Kochenderfer, M.J. Policy compression for aircraft collision avoidance systems. In Proceedings of the 35th IEEE/AIAA Digital Avionics Systems Conference (2016).
7. Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J. Reluplex: An efficient smt solver for verifying deep neural networks. In Proceedings of the 29th International Conference on Computer Aided Verification (2017).
8. LeCun, Y., Cortes, C., Burges, C.J. MNIST handwritten digit database. 2010.
9. Liu, M.-Y., Breuel, T., Kautz, J. Unsupervised image-to-image translation networks. In Advances in Neural Information Processing Systems (2017).
10. Odena, A., Goodfellow, I. Tensorfuzz: Debugging neural networks with coverage-guided fuzzing. arXiv preprint arXiv:1807.10875 (2018).
11. Pei, K., Cao, Y., Yang, J., Jana, S. Towards practical verification of machine learning: The case of computer vision systems. arXiv preprint arXiv:1712.01785 (2017).
12. Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations (2015).
13. Tian, Y., Pei, K., Jana, S., Ray, B. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th International Conference on Software Engineering, ACM (2018), 303--314
14. Šrndic, N., Laskov, P. Practical evasion of a learning-based classifier: a case study. In Proceedings of the 35th IEEE Symposium on Security and Privacy (2014).
15. Wang, S., Pei, K., Whitehouse, J., Yang, J., Jana, S. Efficient formal safety analysis of neural networks. In Advances in Neural Information Processing Systems (2018).
16. Wang, S., Pei, K., Whitehouse, J., Yang, J., Jana, S. Formal security analysis of neural networks using symbolic intervals. In 27th USENIX Security Symposium (2018).
17. Wong, E., Kolter, Z. Provable defenses against adversarial examples via the convex outer adversarial polytope. In International Conference on Machine Learning (2018).
18. Yosinski, J., Clune, J., Fuchs, T., Lipson, H. Understanding neural networks through deep visualization. In 2015 ICML Workshop on Deep Learning (2015).

Index Terms

• Computer systems organization

  • Architectures

    • Other architectures

  • Dependable and fault-tolerant systems and networks

    • Reliability

• Computing methodologies

  • Machine learning

    • Machine learning approaches

• Software and its engineering

  • Software creation and management

    • Software verification and validation