Abstract
As deep learning-based computer vision is widely used in IoT devices, it is especially critical to ensure its security. Among the attacks against deep neural networks, adversarial attacks are a stealthy means of attack, which can mislead model decisions during the testing phase. Therefore, the exploration of adversarial attacks can help to understand the vulnerability of models in advance and make targeted defense.
Existing unrestricted adversarial attacks beyond the \(\ell _p\) norm often require additional models to be both adversarial and imperceptible, which leads to a high computational cost and task-specific design. Inspired by the observation that models exhibit unexpected vulnerability to changes in illumination, we develop Adversarial Illumination Attack (AIA), an unrestricted adversarial attack that imposes large but imperceptible alterations to the image.
The core of the attack lies in simulating adversarial illumination through Planckian jitter, of which the effectiveness comes from a causal chain where the attacker misleads the model by manipulating the confusion factor. We propose an efficient approach to generate adversarial samples without additional models by image gradient regularization. We validate the effectiveness of adversarial illumination in the face of black-box models, data preprocessing, and adversarially trained models through extensive experiments. Experiment results confirm that AIA can be both a lightweight unrestricted attack and a plug-in to boost the effectiveness of other attacks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xiao, Z., et al.: Trajdata: on vehicle trajectory collection with commodity plug-and-play OBU devices. IEEE Internet Things J. 7(9), 9066–9079 (2020)
Li, J., et al.: Drive2friends: inferring social relationships from individual vehicle mobility data. IEEE Internet Things J. 7(6), 5116–5127 (2020)
Long, W., et al.: Unified spatial-temporal neighbor attention network for dynamic traffic prediction. IEEE Trans. Veh. Technol. (2022)
Huang, Y., Xiao, Z., Yu, X., Wang, D., Havyarimana, V., Bai, J.: Road network construction with complex intersections based on sparsely sampled private car trajectory data. ACM Trans. Knowl. Discov. Data (TKDD) 13(3), 1–28 (2019)
Jiang, H., Xiao, Z., Li, Z., Xu, J., Zeng, F., Wang, D.: An energy-efficient framework for internet of things underlaying heterogeneous small cell networks. IEEE Trans. Mob. Comput. 21(1), 31–43 (2020)
Xiao, Z., et al.: Resource management in UAV-assisted MEC: state-of-the-art and open challenges. Wireless Netw. 28(7), 3305–3322 (2022)
Dai, X.: Task co-offloading for d2d-assisted mobile edge computing in industrial internet of things. IEEE Trans. Ind. Inform. 19(1), 480–490 (2022)
Zeng, F., Li, Q., Xiao, Z., Havyarimana, V., Bai, J.: A price-based optimization strategy of power control and resource allocation in full-duplex heterogeneous macrocell-femtocell networks. IEEE Access 6, 42004–42013 (2018)
Xiao, Z., et al.: A joint information and energy cooperation framework for CR-enabled macro-femto heterogeneous networks. IEEE Internet Things J. 7(4), 2828–2839 (2019)
Szegedy, C.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Carlini, N., Wagner, D.: Audio adversarial examples: targeted attacks on speech-to-text. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 1–7. IEEE (2018)
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)
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)
Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185–9193 (2018)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Sharif, M., Bauer, L., Reiter, M.K.: On the suitability of LP-norms for creating and preventing adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1605–1613 (2018)
Liu, F., Zhang, C., Zhang, H.: Towards transferable unrestricted adversarial examples with minimum changes. arXiv preprint arXiv:2201.01102 (2022)
Xiang, T., Liu, H., Guo, S., Gan, Y., Liao, X.: Egm: an efficient generative model for unrestricted adversarial examples. ACM Trans. Sens. Netw. (TOSN) 18(4), 1–25 (2022)
Zhao, Z., Liu, Z., Larson, M.: Adversarial color enhancement: generating unrestricted adversarial images by optimizing a color filter. arXiv preprint arXiv:2002.01008 (2020)
Xu, Q., Tao, G., Cheng, S., Zhang, X.: Towards feature space adversarial attack by style perturbation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 10523–10531 (2021)
Duan, R., Ma, X., Wang, Y., Bailey, J., Qin, A.K., Yang, Y.: Adversarial camouflage: hiding physical-world attacks with natural styles. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1000–1008 (2020)
Laidlaw, C., Singla, S., Feizi, S.: Perceptual adversarial robustness: defense against unseen threat models. In: ICLR (2021)
Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574–2582 (2016)
Chen, P.-Y., Sharma, Y., Zhang, H., Yi, J., Hsieh, C.-J.: Ead: elastic-net attacks to deep neural networks via adversarial examples. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
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)
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828–841 (2019)
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)
Liu, X., Yang, H., Liu, Z., Song, L., Li, H., Chen, Y.: Dpatch: an adversarial patch attack on object detectors. arXiv preprint arXiv:1806.02299 (2018)
Laidlaw, C., Feizi, S.: Functional adversarial attacks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Alaifari, R., Alberti, G.S., Gauksson, T.: Adef: an iterative algorithm to construct adversarial deformations. arXiv preprint arXiv:1804.07729 (2018)
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., Song, D.: Spatially transformed adversarial examples. arXiv preprint arXiv:1801.02612 (2018)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)
Hosseini, H., Poovendran, R.: Semantic adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1614–1619 (2018)
Shamsabadi, A.S., Sanchez-Matilla, R., Cavallaro, A.: Colorfool: semantic adversarial colorization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1151–1160 (2020)
Bhattad, A., Chong, M.J., Liang, K., Li, B., Forsyth, D.A.: Unrestricted adversarial examples via semantic manipulation. arXiv preprint arXiv:1904.06347 (2019)
Das, N.: Shield: fast, practical defense and vaccination for deep learning using jpeg compression. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 196–204 (2018)
Guo, C., Rana, M., Cisse, M., Van Der Maaten, L.: Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)
Meng, D., Chen, H.: Magnet: a two-pronged defense against adversarial examples. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 135–147 (2017)
Raghunathan, A., Xie, S.M., Yang, F., Duchi, J., Liang, P.: Understanding and mitigating the tradeoff between robustness and accuracy. arXiv preprint arXiv:2002.10716 (2020)
Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning, pp. 7472–7482. PMLR (2019)
Wu, D., Xia, S.-T., Wang, Y.: Adversarial weight perturbation helps robust generalization. Adv. Neural. Inf. Process. Syst. 33, 2958–2969 (2020)
Pang, T., Yang, X., Dong, Y., Xu, K., Zhu, J., Su, H.: Boosting adversarial training with hypersphere embedding. Adv. Neural. Inf. Process. Syst. 33, 7779–7792 (2020)
Zini, S., Buzzelli, M., Twardowski, B., van de Weijer, J.: Planckian jitter: enhancing the color quality of self-supervised visual representations. arXiv preprint arXiv:2202.07993 (2022)
Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 (2019)
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
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)
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
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)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Zhang, H., Wang, J.: Defense against adversarial attacks using feature scattering-based adversarial training. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, K., Chen, Y., Xu, W. (2023). Demons Hidden in the Light: Unrestricted Adversarial Illumination Attacks. In: Xiao, Z., Zhao, P., Dai, X., Shu, J. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 478. Springer, Cham. https://doi.org/10.1007/978-3-031-28990-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-28990-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28989-7
Online ISBN: 978-3-031-28990-3
eBook Packages: Computer ScienceComputer Science (R0)