Skip to main content

GradAuto: Energy-Oriented Attack on Dynamic Neural Networks

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

Included in the following conference series:

Abstract

Dynamic neural networks could adapt their structures or parameters based on different inputs. By reducing the computation redundancy for certain samples, it can greatly improve the computational efficiency without compromising the accuracy. In this paper, we investigate the robustness of dynamic neural networks against energy-oriented attacks. We present a novel algorithm, named GradAuto, to attack both dynamic depth and dynamic width models, where dynamic depth networks reduce redundant computation by skipping some intermediate layers while dynamic width networks adaptively activate a subset of neurons in each layer. Our GradAuto carefully adjusts the direction and the magnitude of the gradients to efficiently find an almost imperceptible perturbation for each input, which will activate more computation units during inference. In this way, GradAuto effectively boosts the computational cost of models with dynamic architectures. Compared to previous energy-oriented attack techniques, GradAuto obtains the state-of-the-art result and recovers 100% dynamic network reduced FLOPs on average for both dynamic depth and dynamic width models. Furthermore, we demonstrate that GradAuto offers us great control over the attacking process and could serve as one of the keys to unlock the potential of the energy-oriented attack. Please visit https://github.com/JianhongPan/GradAuto for code.

J. Pan and Q. Zheng— Both authors contributed equally to this research.

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

Access this chapter

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

References

  1. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  2. Bejnordi, B.E., Blankevoort, T., Welling, M.: Batch-shaping for learning conditional channel gated networks. arXiv preprint arXiv:1907.06627 (2019)

  3. Bengio, E., Bacon, P.L., Pineau, J., Precup, D.: Conditional computation in neural networks for faster models. arXiv preprint arXiv:1511.06297 (2015)

  4. Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

  5. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  6. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39–57. IEEE (2017)

    Google Scholar 

  7. Chen, J., Zhu, Z., Li, C., Zhao, Y.: Self-adaptive network pruning. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11953, pp. 175–186. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36708-4_15

    Chapter  Google Scholar 

  8. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  9. Cheng, J., Wang, P., Li, G., Hu, Q., Lu, H.: Recent advances in efficient computation of deep convolutional neural networks. arXiv preprint arXiv:1802.00939 (2018)

  10. Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 (2017)

  11. Cho, K., Bengio, Y.: Exponentially increasing the capacity-to-computation ratio for conditional computation in deep learning. arXiv preprint arXiv:1406.7362 (2014)

  12. Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  13. Eigen, D., Ranzato, M., Sutskever, I.: Learning factored representations in a deep mixture of experts. arXiv preprint arXiv:1312.4314 (2013)

  14. Figurnov, M., Collins, M.D., Zhu, Y., Zhang, L., Huang, J., Vetrov, D., Salakhutdinov, R.: Spatially adaptive computation time for residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1039–1048 (2017)

    Google Scholar 

  15. Gao, X., Zhao, Y., Dudziak, Ł., Mullins, R., Xu, C.Z.: Dynamic channel pruning: Feature boosting and suppression. arXiv preprint arXiv:1810.05331 (2018)

  16. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision pp. 1440–1448 (2015)

    Google Scholar 

  17. Graves, A.: Adaptive computation time for recurrent neural networks. arXiv preprint arXiv:1603.08983 (2016)

  18. Guo, Q., Yu, Z., Wu, Y., Liang, D., Qin, H., Yan, J.: Dynamic recursive neural network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5147–5156 (2019)

    Google Scholar 

  19. Han, Y., Huang, G., Song, S., Yang, L., Wang, H., Wang, Y.: Dynamic neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021)

    Google Scholar 

  20. Haque, M., Chauhan, A., Liu, C., Yang, W.: ILFO: adversarial attack on adaptive neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  21. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  23. Hong, S., Kaya, Y., Modoranu, I.V., Dumitras, T.: A panda? No, it’s a sloth: slowdown attacks on adaptive multi-exit neural network inference. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=9xC2tWEwBD

  24. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)

    Google Scholar 

  25. Hua, W., Zhou, Y., De Sa, C.M., Zhang, Z., Suh, G.E.: Channel gating neural networks. In: Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79–87 (1991)

    Article  Google Scholar 

  28. Jin, Q., Yang, L., Liao, Z.: AdaBits: Neural network quantization with adaptive bit-widths. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2146–2156 (2020)

    Google Scholar 

  29. Leroux, S., Molchanov, P., Simoens, P., Dhoedt, B., Breuel, T., Kautz, J.: Iamnn: iterative and adaptive mobile neural network for efficient image classification. arXiv preprint arXiv:1804.10123 (2018)

  30. Li, C., Wang, G., Wang, B., Liang, X., Li, Z., Chang, X.: Dynamic slimmable network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8607–8617 (2021)

    Google Scholar 

  31. Li, Y., Song, L., Chen, Y., Li, Z., Zhang, X., Wang, X., Sun, J.: Learning dynamic routing for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8553–8562 (2020)

    Google Scholar 

  32. Lin, J., Rao, Y., Lu, J., Zhou, J.: Runtime neural pruning. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  33. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  34. Liu, C., Wang, Y., Han, K., Xu, C., Xu, C.: Learning instance-wise sparsity for accelerating deep models. arXiv preprint arXiv:1907.11840 (2019)

  35. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  36. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  37. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. arXiv preprint arXiv:2201.03545 (2022)

  38. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  39. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  40. Shen, J., Wang, Y., Xu, P., Fu, Y., Wang, Z., Lin, Y.: Fractional skipping: towards finer-grained dynamic CNN inference. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5700–5708 (2020)

    Google Scholar 

  41. Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)

  42. Tang, Y., et al.: Manifold regularized dynamic network pruning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5018–5028 (2021)

    Google Scholar 

  43. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  44. Veit, A., Belongie, S.: Convolutional networks with adaptive inference graphs. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–18 (2018)

    Google Scholar 

  45. Wang, X., Yu, F., Dou, Z.Y., Darrell, T., Gonzalez, J.E.: SkipNet: learning dynamic routing in convolutional networks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  46. Wang, Y., et al.: Dual dynamic inference: enabling more efficient, adaptive, and controllable deep inference. IEEE J. Select. Top. Signal Process. 14(4), 623–633 (2020)

    Article  Google Scholar 

  47. Xia, W., Yin, H., Dai, X., Jha, N.K.: Fully dynamic inference with deep neural networks. IEEE Trans. Emerg. Top. Comput. 10, 962–972 (2021)

    Google Scholar 

  48. Yang, B., Bender, G., Le, Q.V., Ngiam, J.: CondConv: conditionally parameterized convolutions for efficient inference. In: Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  49. Yu, H., Li, H., Shi, H., Huang, T.S., Hua, G., et al.: Any-precision deep neural networks. arXiv preprint arXiv:1911.07346 1 (2019)

Download references

Acknowledgements

This work is supported by National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-100E-2020-065), MOE Tier 1 Grant, and SUTD Startup Research Grant. The research is also supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Liu .

Editor information

Editors and Affiliations

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

Pan, J., Zheng, Q., Fan, Z., Rahmani, H., Ke, Q., Liu, J. (2022). GradAuto: Energy-Oriented Attack on Dynamic Neural Networks. 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 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19772-7_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19771-0

  • Online ISBN: 978-3-031-19772-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics