Skip to main content

Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13665))

Included in the following conference series:

Abstract

Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method termed black-box dissector, which consists of two erasing-based modules. One is a CAM-driven erasing strategy that is designed to increase the information capacity hidden in hard labels from the victim model. The other is a random-erasing-based self-knowledge distillation module that utilizes soft labels from the substitute model to mitigate overfitting. Extensive experiments on four widely-used datasets consistently demonstrate that our method outperforms state-of-the-art methods, with an improvement of at most \(8.27\%\). We also validate the effectiveness and practical potential of our method on real-world APIs and defense methods. Furthermore, our method promotes other related tasks, i.e., transfer adversarial attacks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    For the purpose of protecting privacy, we hide the specific information of the victim model.

  2. 2.

    https://github.com/garythung/trashnet.

References

  1. Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G.E., Hinton, G.E.: Large scale distributed neural network training through online distillation. arXiv preprint arXiv:1804.03235 (2018)

  2. Barbalau, A., Cosma, A., Ionescu, R.T., Popescu, M.: Black-box ripper: copying black-box models using generative evolutionary algorithms. In: NeurIPS (2020)

    Google Scholar 

  3. DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)

  4. Dong, X., Luu, A.T., Ji, R., Liu, H.: Towards robustness against natural language word substitutions. In: ICLR (2021)

    Google Scholar 

  5. Ducoffe, M., Precioso, F.: Adversarial active learning for deep networks: a margin based approach. In: ICML (2018)

    Google Scholar 

  6. Fang, S., Li, J., Lin, X., Ji, R.: Learning to learn transferable attack. In: AAAI (2022)

    Google Scholar 

  7. Furlanello, T., Lipton, Z.C., Tschannen, M., Itti, L., Anandkumar, A.: Born again neural networks. In: ICML (2018)

    Google Scholar 

  8. Gong, X., Chen, Y., Yang, W., Mei, G., Wang, Q.: Inversenet: augmenting model extraction attacks with training data inversion. In: IJCAI (2021)

    Google Scholar 

  9. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)

    Google Scholar 

  10. Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML (2017)

    Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  12. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS Deep Learning Workshop (2015)

    Google Scholar 

  13. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)

    Google Scholar 

  14. Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High accuracy and high fidelity extraction of neural networks. In: 29th Usenix Security (2020)

    Google Scholar 

  15. Kariyappa, S., Prakash, A., Qureshi, M.: Maze: data-free model stealing attack using zeroth-order gradient estimation. arXiv preprint arXiv:2005.03161 (2020)

  16. Kariyappa, S., Qureshi, M.K.: Defending against model stealing attacks with adaptive misinformation. In: CVPR (2020)

    Google Scholar 

  17. Kim, K., Ji, B., Yoon, D., Hwang, S.: Self-knowledge distillation: a simple way for better generalization. arXiv preprint arXiv:2006.12000 (2020)

  18. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

    Google Scholar 

  19. Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: SIGIR (1994)

    Google Scholar 

  20. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)

    Google Scholar 

  21. Maini, P., Yaghini, M., Papernot, N.: Dataset inference: ownership resolution in machine learning. In: ICLR (2021)

    Google Scholar 

  22. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)

    Google Scholar 

  23. Orekondy, T., Schiele, B., Fritz, M.: Knockoff nets: stealing functionality of black-box models. In: CVPR (2019)

    Google Scholar 

  24. Orekondy, T., Schiele, B., Fritz, M.: Prediction poisoning: towards defenses against dnn model stealing attacks. In: ICLR (2019)

    Google Scholar 

  25. Pal, S., Gupta, Y., Shukla, A., Kanade, A., Shevade, S., Ganapathy, V.: Activethief: model extraction using active learning and unannotated public data. In: AAAI (2020)

    Google Scholar 

  26. Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: ACM AsiACCS (2017)

    Google Scholar 

  27. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV 115, 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  28. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)

    Google Scholar 

  29. Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: ICLR (2018)

    Google Scholar 

  30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)

    Google Scholar 

  31. Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR (2014)

    Google Scholar 

  32. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)

    Google Scholar 

  33. Wang, X., Xiang, Y., Gao, J., Ding, J.: Information laundering for model privacy. In: ICLR (2021)

    Google Scholar 

  34. Yang, J., Jiang, Y., Huang, X., Ni, B., Zhao, C.: Learning black-box attackers with transferable priors and query feedback. In: NeurIPS (2020)

    Google Scholar 

  35. Yu, H., Yang, K., Zhang, T., Tsai, Y.Y., Ho, T.Y., Jin, Y.: Cloudleak: large-scale deep learning models stealing through adversarial examples. In: NDSS (2020)

    Google Scholar 

  36. Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: ICLR (2017)

    Google Scholar 

  37. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: ICLR (2017)

    Google Scholar 

  38. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: AAAI (2020)

    Google Scholar 

  39. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)

    Google Scholar 

  40. Zhou, M., Wu, J., Liu, Y., Liu, S., Zhu, C.: Dast: data-free substitute training for adversarial attacks. In: CVPR (2020)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Science Fund for Distinguished Young Scholars (No.62025603), the National Natural Science Foundation of China (No. U21B2037, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, and No. 62002305), Guangdong Basic and Applied Basic Research Foundation (No. 2019B1515120049), and the Natural Science Foundation of Fujian Province of China (No. 2021J01002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rongrong Ji .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 324 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y. et al. (2022). Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20065-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20064-9

  • Online ISBN: 978-3-031-20065-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics