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In the recent quest for trustworthy neural networks, we present Spiking Neural Network (SNN) as a potential candidate for inherent robustness against adversarial attacks.
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Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.C., Pfeiffer, M.: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: 2015 International Joint Conference on Neural Networks (IJCNN) . pp. 1–8 (2015)
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Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP) . pp. 39–57 (2017)
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Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: International Conference on Learning Representations (ICLR) (2017)
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (ICLR) (2018)
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Tram e ` \grave{e} r, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: Ensemble adversarial training: Attacks and defenses. In: International Conference on Learning Representations (ICLR) (2018)
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Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in Neuroscience 12
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Bagheri, A., Simeone, O., Rajendran, B.: Adversarial training for probabilistic spiking neural networks. In: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2018)
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Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in Neural Information Processing Systems . pp. 787–797 (2018)
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Buckman, J., Roy, A., Raffel, C., Goodfellow, I.: Thermometer encoding: One hot way to resist adversarial examples. In: International Conference on Learning Representations (ICLR) (2018)
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Dhillon, G.S., Azizzadenesheli, K., Lipton, Z.C., Bernstein, J., Kossaifi, J., Khanna, A., Anandkumar, A.: Stochastic activation pruning for robust adversarial defense. In: International Conference on Learning Representations (ICLR) (2018)
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Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical world attacks on deep learning visual classifications. In: CVPR (2018)
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Guo, C., Rana, M., Cisse, M., van der Maaten, L.: Countering adversarial images using input transformations (2018)
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Marchisio, A., Nanfa, G., Khalid, F., Hanif, M.A., Martina, M., Shafique, M.: SNN under attack: are spiking deep belief networks vulnerable to adversarial examples? (2019)
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Olin-Ammentorp, W., Beckmann, K., Schuman, C.D., Plank, J.S., Cady, N.C.: Stochasticity and robustness in spiking neural networks (2019)
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Panda, P., Chakraborty, I., Roy, K.: Discretization based solutions for secure machine learning against adversarial attacks. IEEE Access 7
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Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: VGG and residual architectures. Frontiers in neuroscience 13:95
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Sharmin, S., Panda, P., Sarwar, S.S., Lee, C., Ponghiran, W., Roy, K.: A comprehensive analysis on adversarial robustness of spiking neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN) (2019)
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Rathi, N., Srinivasan, G., Panda, P., Roy, K.: Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. In: International Conference on Learning Representations (ICLR) (2020)
2020
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