LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings (7 2017), http://arxiv.org/abs/1607.02533

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2017

Cited alongside, same era.

Mandt, S., Hof Fman, M.D., Blei, D.M.: Stochastic Gradient Descent as Approximate Bayesian Inference. Journal of Machine Learning Research 18

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2017

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Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: A General Framework for Adversarial Examples with Objectives. ACM Transactions on Privacy and Security 22

2017

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Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: The Space of Transferable Adversarial Examples (4 2017), http://arxiv.org/abs/1704.03453

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2017

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Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting Adversarial Attacks with Momentum. In: CVPR. pp. 9185–9193 (10 2018). https://doi.org/10.1109/CVPR.2018.00957

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Gur-Ari, G., Roberts, D.A., Dyer, E.: Gradient Descent Happens in a Tiny Subspace (12 2018), http://arxiv.org/abs/1812.04754

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2018

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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: NIPS, pp. 8024–8035 (2019), http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

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Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., Yuille, A.L.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2019-June, pp. 2725–2734 (3 2019). https://doi.org/10.1109/CVPR.2019.00284

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Charles, Z., Rosenberg, H., Papailiopoulos, D.: A geometric perspective on the transferability of adversarial directions. In: AISTATS 2019 (11 2020), http://arxiv.org/abs/1811.03531

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2020

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Yao, Z., Gholami, A., Keutzer, K., Mahoney, M.W.: PyHessian: Neural Networks Through the Lens of the Hessian. Big Data 2020 pp. 581–590 (12 2019). https://doi.org/10.1109/BigData50022.2020.9378171

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Zhang, S., Reid, I., Pérez, G.V., Louis, A.: Why flatness does and does not correlate with generalization for deep neural networks (3 2021), http://arxiv.org/abs/2103.06219

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2021

Later among the works it cites.