Abstract
Much progress in interpretable AI is built around scenarios where the user, one who interprets the model, has a full ownership of the model to be diagnosed. The user either owns the training data and computing resources to train an interpretable model herself or owns a full access to an already trained model to be interpreted post-hoc. In this chapter, we consider a less investigated scenario of diagnosing black-box neural networks, where the user can only send queries and read off outputs. Black-box access is a common deployment mode for many public and commercial models, since internal details, such as architecture, optimisation procedure, and training data, can be proprietary and aggravate their vulnerability to attacks like adversarial examples. We propose a method for exposing internals of black-box models and show that the method is surprisingly effective at inferring a diverse set of internal information. We further show how the exposed internals can be exploited to strengthen adversarial examples against the model. Our work starts an important discussion on the security implications of diagnosing deployed models with limited accessibility. The code is available at goo.gl/MbYfsv.
Supported by German Research Foundation (DFG CRC 1223).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
kennen means “to know” in German, and “to dig out” in Korean.
- 3.
References
Ateniese, G., Mancini, L.V., Spognardi, A., Villani, A., Vitali, D., Felici, G.: Hacking smart machines with smarter ones: how to extract meaningful data from machine learning classifiers. Int. J. Secur. Netw. 10(3), 137–150 (2015)
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM (2015)
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 15–26. ACM, New York (2017)
Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. In: Advances in Neural Information Processing Systems, pp. 6967–6976 (2017)
Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3449–3457. IEEE (2017)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)
Hayes, J., Danezis, G.: Machine learning as an adversarial service: learning black-box adversarial examples. CoRR abs/1708.05207 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hendricks, L.A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., Darrell, T.: Generating visual explanations. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_1
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)1mb model size. CoRR abs/1602.07360 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 448–456. PMLR, Lille (2015). http://proceedings.mlr.press/v37/ioffe15.html
Kamruzzaman, S.M., Islam, M.M.: An algorithm to extract rules from artificial neural networks for medical diagnosis problems. CoRR abs/1009.4566 (2010)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Letham, B., Rudin, C., McCormick, T.H., Madigan, D., et al.: Interpretable classifiers using rules and bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350–1371 (2015)
Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 30:31–30:57 (2018)
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: International Conference on Learning Representations (2017)
van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5188–5196 (2015)
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765–1773 (2017)
Narodytska, N., Kasiviswanathan, S.P.: Simple black-box adversarial perturbations for deep networks. CoRR abs/1612.06299 (2017)
Neumann, J.: Zur Theorie der Gesellschaftsspiele. Math. Ann. 100, 295–320 (1928). http://eudml.org/doc/159291
Oh, S.J., Augustin, M., Schiele, B., Fritz, M.: Towards reverse-engineering black-box neural networks. In: International Conference on Learning Representations (ICLR) (2018)
Oh, S.J., Fritz, M., Schiele, B.: Adversarial image perturbation for privacy protection a game theory perspective. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1491–1500. IEEE (2017)
Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. CoRR abs/1605.07277 (2016)
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 506–519. ACM (2017)
Poursaeed, O., Katsman, I., Gao, B., Belongie, S.: Generative adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4422–4431 (2018)
Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2016)
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3–18. IEEE (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs/1312.6034 (2013)
Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (2014)
Tramèr, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: 25th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 16), pp. 601–618 (2016)
Yosinski, J., Clune, J., Nguyen, A.M., Fuchs, T.J., Lipson, H.: Understanding neural networks through deep visualization. CoRR abs/1506.06579 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Oh, S.J., Schiele, B., Fritz, M. (2019). Towards Reverse-Engineering Black-Box Neural Networks. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L., Müller, KR. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science(), vol 11700. Springer, Cham. https://doi.org/10.1007/978-3-030-28954-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-28954-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28953-9
Online ISBN: 978-3-030-28954-6
eBook Packages: Computer ScienceComputer Science (R0)