Advertisement

Too Big to FAIL: What You Need to Know Before Attacking a Machine Learning System

  • Tudor DumitraşEmail author
  • Yiğitcan Kaya
  • Radu Mărginean
  • Octavian Suciu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11286)

Abstract

There is an emerging arms race in the field of adversarial machine learning (AML). Recent results suggest that machine learning (ML) systems are vulnerable to a wide range of attacks; meanwhile, there are no systematic defenses. In this position paper we argue that to make progress toward such defenses, the specifications for machine learning systems must include precise adversary definitions—a key requirement in other fields, such as cryptography or network security. Without common adversary definitions, new AML attacks risk making strong and unrealistic assumptions about the adversary’s capabilities. Furthermore, new AML defenses are evaluated based on their robustness against adversarial samples generated by a specific attack algorithm, rather than by a general class of adversaries. We propose the FAIL adversary model, which describes the adversary’s knowledge and control along four dimensions: data Features, learning Algorithms, training Instances and crafting Leverage. We analyze several common assumptions, often implicit, from the AML literature, and we argue that the FAIL model can represent and generalize the adversaries considered in these references. The FAIL model allows us to consider a range of adversarial capabilities and enables systematic comparisons of attacks against ML systems, providing a clearer picture of the security threats that these attacks raise. By evaluating how much a new AML attack’s success depends on the strength of the adversary along each of the FAIL dimensions, researchers will be able to reason about the real effectiveness of the attack. Additionally, such evaluations may suggest promising directions for investigating defenses against the ML threats.

Keywords

Machine learning Adversary model 

References

  1. 1.
    Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.: DREBIN: effective and explainable detection of android malware in your pocket. In: 21st Annual Network and Distributed System Security Symposium, NDSS 2014, San Diego, California, USA, 23–26 February 2014 (2014). https://www.ndss-symposium.org/ndss2014/drebin-effective-and-explainable-detection-android-malware-your-pocket
  2. 2.
    Barreno, M., Nelson, B., Joseph, A.D., Tygar, J.D.: The security of machine learning. Mach. Learn. 81, 121–148 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Biggio, B., Corona, I., Fumera, G., Giacinto, G., Roli, F.: Bagging classifiers for fighting poisoning attacks in adversarial classification tasks. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 350–359. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21557-5_37CrossRefGoogle Scholar
  4. 4.
    Biggio, B., et al.: Evasion attacks against machine learning at test time. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 387–402. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40994-3_25CrossRefGoogle Scholar
  5. 5.
    Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012)
  6. 6.
    Carlini, N., et al.: Hidden voice commands. In: USENIX Security Symposium, pp. 513–530 (2016)Google Scholar
  7. 7.
    Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, 22–26 May 2017, pp. 39–57 (2017).  https://doi.org/10.1109/SP.2017.49
  8. 8.
    Chau, D.H.P., Nachenberg, C., Wilhelm, J., Wright, A., Faloutsos, C.: Polonium: tera-scale graph mining for malware detection. In: SIAM International Conference on Data Mining (SDM), Mesa, AZ, April 2011. http://www.cs.cmu.edu/~dchau/polonium/polonium_kdd_ldmta_2010.pdf
  9. 9.
    Chung, J.S., Senior, A., Vinyals, O., Zisserman, A.: Lip reading sentences in the wild. arXiv preprint arXiv:1611.05358 v2 (2016)
  10. 10.
    Cretu, G.F., Stavrou, A., Locasto, M.E., Stolfo, S.J., Keromytis, A.D.: Casting out demons: sanitizing training data for anomaly sensors. In: IEEE Symposium on Security and Privacy, SP 2008, pp. 81–95. IEEE (2008)Google Scholar
  11. 11.
    Dalvi, N., Domingos, P., Sanghai, S., Verma, D., et al.: Adversarial classification. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 99–108. ACM (2004)Google Scholar
  12. 12.
    Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., Ristenpart, T.: Privacy in pharmacogenetics: an end-to-end case study of personalized warfarin dosing. In: 23rd USENIX Security Symposium (USENIX Security 2014), pp. 17–32 (2014)Google Scholar
  13. 13.
    Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201–210 (2016)Google Scholar
  14. 14.
    Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
  15. 15.
    Gu, T., Dolan-Gavitt, B., Garg, S.: Badnets: identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017)
  16. 16.
    Hearn, M.: Abuse at scale. In: RIPE 64, Ljublijana, Slovenia, April 2012. https://ripe64.ripe.net/archives/video/25/
  17. 17.
    Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. arXiv preprint arXiv:1703.04730 (2017)
  18. 18.
    Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)
  19. 19.
    Liu, Y., et al.: Trojaning attack on neural networks. Technical report 17-002. Purdue University (2017)Google Scholar
  20. 20.
    Lowd, D., Meek, C.: Adversarial learning. In: Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 641–647. ACM (2005)Google Scholar
  21. 21.
    Muñoz-González, L., et al.: Towards poisoning of deep learning algorithms with back-gradient optimization. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 27–38. ACM (2017)Google Scholar
  22. 22.
    Nelson, B., et al.: Exploiting machine learning to subvert your spam filter. In: Proceedings of the 1st USENIX Workshop on Large-Scale Exploits and Emergent Threats, LEET 2008, pp. 7:1–7:9. USENIX Association, Berkeley (2008). http://dl.acm.org/citation.cfm?id=1387709.1387716
  23. 23.
    Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against deep learning systems using adversarial examples. arXiv preprint arXiv:1602.02697 (2016)
  24. 24.
    Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387. IEEE (2016)Google Scholar
  25. 25.
    Papernot, N., McDaniel, P.D., Goodfellow, I.J.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. CoRR abs/1605.07277 (2016). http://arxiv.org/abs/1605.07277
  26. 26.
    Papernot, N., McDaniel, P.D., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: IEEE Symposium on Security and Privacy, SP 2016, San Jose, CA, USA, 22–26 May 2016, pp. 582–597 (2016),  https://doi.org/10.1109/SP.2016.41
  27. 27.
    Papernot, N., McDaniel, P.D., Goodfellow, I.J., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against deep learning systems using adversarial examples. In: ACM Asia Conference on Computer and Communications Security, Abu Dhabi, UAE (2017). http://arxiv.org/abs/1602.02697
  28. 28.
    Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1528–1540. ACM (2016)Google Scholar
  29. 29.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556
  30. 30.
    Steinhardt, J., Koh, P.W.W., Liang, P.S.: Certified defenses for data poisoning attacks. In: Advances in Neural Information Processing Systems, pp. 3520–3532 (2017)Google Scholar
  31. 31.
    Suciu, O., Marginean, R., Kaya, Y., Daume III, H., Dumitras, T.: When does machine learning FAIL? Generalized transferability for evasion and poisoning attacks. In: 27th USENIX Security Symposium (USENIX Security 2018), pp. 1299–1316. USENIX Association, Baltimore (2018). https://www.usenix.org/conference/usenixsecurity18/presentation/suciu
  32. 32.
    Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
  33. 33.
    Tamersoy, A., Roundy, K., Chau, D.H.: Guilt by association: large scale malware detection by mining file-relation graphs. In: KDD (2014)Google Scholar
  34. 34.
    Tramèr, F., Zhang, F., Juels, A., Reiter, M., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: 25th USENIX Security Symposium (USENIX Security 2016). USENIX Association, Austin, August 2016. https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/tramer
  35. 35.
    Xu, W., Evans, D., Qi, Y.: Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 (2017)
  36. 36.
    Xu, W., Qi, Y., Evans, D.: Automatically evading classifiers. In: Proceedings of the 2016 Network and Distributed Systems Symposium (2016)Google Scholar
  37. 37.
    Yang, C., Wu, Q., Li, H., Chen, Y.: Generative poisoning attack method against neural networks. arXiv preprint arXiv:1703.01340 (2017)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tudor Dumitraş
    • 1
    Email author
  • Yiğitcan Kaya
    • 1
  • Radu Mărginean
    • 1
  • Octavian Suciu
    • 1
  1. 1.University of MarylandCollege ParkUSA

Personalised recommendations