Developing Hessian–Free Second–Order Adversarial Examples for Adversarial Training
By:
References
- Andriushchenko, M., Croce, F., Marion, N.F. and Hein, M. (2020). Square attack: A query-efficient black-box adversarial attack via random search, European Conference on Computer Vision, Glasgow, UK, pp. 484–501.
- Anil, R., Gupta, V., Koren, T., Regan, K. and Singer, Y. (2020). Second-order optimization made practical, arXiv: 2002.09018.
- Athalye, A., Carlini, N. and Wagner, D. (2018). Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples, International Conference on Machine Learning, Stockholm, Sweden, pp. 274–283.
- Badjie, B., Cec´ılio, J. and Casimiro, A. (2023). Denoising autoencoder-based defensive distillation as an adversarial robustness algorithm, CoRR: abs/2303.15901.
- Bertolace, A., Gatsis, K. and Margellos, K. (2024). Robust optimization for adversarial learning with finite sample complexity guarantees, CoRR: abs/2403.15207.
- Croce, F. and Hein, M. (2020a). Minimally distorted adversarial examples with a fast adaptive boundary attack, International Conference on Machine Learning, pp. 2196–2205, (virtual event).
- Croce, F. and Hein, M. (2020b). Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks, International Conference on Machine Learning, pp. 2206–2216, (virtual event).
- Ding, X., Zhang, X., Zhou, Y., Han, J., Ding, G. and Sun, J. (2022). Scaling up your kernels to 31x31: Revisiting large kernel design in CNNs, CoRR: abs/2203.06717.
- Faragallah, O.S., El-Hoseny, H., El-Shafai, W., El-Rahman, W.A., El-Sayed, H.S., El-Rabaie, E.-S.M., El-Samie, F.E.A. and Geweid, G.G.N. (2021). A comprehensive survey analysis for present solutions of medical image fusion and future directions, IEEE Access 9: 11358–11371.
- Fawzi, A., Moosavi-Dezfooli, S., Frossard, P. and Soatto, S. (2018). Empirical study of the topology and geometry of deep networks, 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 3762–3770.
- Ge, Z.,Wang, X., Liu, H., Shang, F. and Liu, Y. (2023). Boosting adversarial transferability by achieving flat local maxima, Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, USA.
- Goodfellow, I., Shlens, J. and Szegedy, C. (2015). Explaining and harnessing adversarial examples, International Conference on Learning Representations, San Diego, USA.
- Guo, S., Li, X., Zhu, P. and Mu, Z. (2023). Ads-detector: An attention-based dual stream adversarial example detection method, Knowledge-Based Systems 265: 110388.
- Heaven, D. (2019). Why deep-learning AIs are so easy to fool, Nature 574(777): 163–166.
- Huang, B., Chen, M., Wang, Y., Lu, J., Cheng, M. and Wang, W. (2023). Boosting accuracy and robustness of student models via adaptive adversarial distillation, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, Canada, pp. 24668–24677.
- Jea, K.C. and Young, D.M. (1980). Generalized conjugate gradient acceleration of non-symmetrizable iterative methods, Linear Algebra and Its Applications 34: 159–194.
- Jetly, S., Lord, N. and Torr, P. (2018). With friend like this, who need adversaries?, 2018 Conference on Neural Information Processing Systems, Montr´eal, Canada, pp. 10772–10782.
- Jin, G., Yi, X., Huang, W., Schewe, S. and Huang, X. (2022). Enhancing adversarial training with second-order statistics of weights, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 15273–15283.
- Jin, G., Yi, X., Wu, D., Mu, R. and Huang, X. (2023). Randomized adversarial training via Taylor expansion, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, Canada, pp. 16447–16457.
- Li, B., Chen, C., Wang, W. and Carin, L. (2018). Second-order adversarial attack and certifiable robustness. arXiv: 1809.03113.
- Li, L. and Spratling, M.W. (2023). Understanding and combating robust overfitting via input loss landscape analysis and regularization, Pattern Recognition 136: 109229.
- Li, T., Wu, Y., Chen, S., Fang, K. and Huang, X. (2022). Subspace adversarial training, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, USA, pp. 13399–13408.
- Liu, X., Kuang, H., Lin, X., Wu, Y. and Ji, R. (2023). CAT: Collaborative adversarial training, CoRR: abs/2303.14922.
- Long, Y., Wen, Y., Han, J., Xu, H., Ren, P., Zhang, W., Zhao, S. and Liang, X. (2023). CAPDET: Unifying dense captioning and open-world detection pretraining, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, Canada, pp. 15233–15243.
- Lu, Y., Ren, H., Chai, W., Velipasalar, S. and Li, Y. (2024). Time-aware and task-transferable adversarial attack for perception of autonomous vehicles, Pattern Recognition Letters 178: 145–152.
- Lyu, C., Huang, K. and Liang, H. (2015). A unified gradient regularization family for adversarial examples, IEEE International Conference on Data Mining, Atlantic City, USA, pp. 301–309.
- Ma, A., Faghri, F., Papernot, N. and Massoud Farahmand, A. (2020). SOAR: Second-order adversarial regularization. arXiv: 2004.01832.
- Madry, A., Makelov, A., Schmidt, L., Tsipras, D. and Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks, arXiv: 1706.06083.
- Moosavi-Dezfooli, S.-M., Fawzi, A., Uesato, J. and Frossard, P. (2019). Robustness via curvature regularization, and vice versa, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, pp. 9070–9078.
- Nilsson, J. and Akenine-M¨oller, T. (2020). Understanding SSIM, arXiv: 2006.13846.
- Pozdnyakov, V., Kovalenko, A., Makarov, I., Drobyshevskiy, M. and Lukyanov, K. (2024). Adversarial attacks and defenses in automated control systems: A comprehensive benchmark, arXiv: 2403.13502.
- Ros, A.S. and Doshi-Velez, F. (2018). Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients, AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 1660–1669.
- Shimonishi, H., MAKI, I., Murase, T. and Murata, M. (2002). Dynamic fair bandwidth allocation for diffserv classes, IEEE International Conference on Communications, ICC 2002, New York, USA, pp. 2348–2352.
- Song, M., Choi, J. and Han, B. (2024). A training-free defense framework for robust learned image compression, CoRR: abs/2401.11902.
- Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. and Fergus, R. (2013). Intriguing properties of neural networks, arXiv: 1312.6199.
- Tejankar, A., Sanjabi, M., Wang, Q., Wang, S., Firooz, H., Pirsiavash, H. and Tan, L. (2023). Defending against patch-based backdoor attacks on self-supervised learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, Canada, pp. 12239–12249.
- Thornton, C. and Bierman, G. (1977). Givens transformation techniques for Kalman filtering, Acta Astronautica 4(7): 847–863.
- Tsiligkaridis, T. and Roberts, J. (2020). Second-order optimization for adversarial robustness and interpretability, arXiv: 2009.04923.
- Wang, H. and Wang, Y. (2022). Self-ensemble adversarial training for improved robustness, 10th International Conference on Learning Representations, ICLR 2022, (virtual event).
- Wu, T., Luo, T. and Wunsch II, D.C. (2024). LRS: Enhancing adversarial transferability through Lipschitz regularized surrogate, Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, Canada, pp. 6135–6143.
- Yang, X., Liu, C., Xu, L., Wang, Y., Dong, Y., Chen, N., Su, H. and Zhu, J. (2023). Towards effective adversarial textured 3D meshes on physical face recognition, IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, pp. 4119–4128.
- Yeom, I. and Reddy, A. (2001). Modeling TCP behavior in a differentiated services network, IEEE/ACM Transactions on Networking 9(1): 31–46.
- Yin, Z., Liu, M., Li, X., Yang, H., Xiao, L. and Zuo, W. (2023). MetaF2N: Blind image super-resolution by learning efficient model adaptation from faces, IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, pp. 12987–12998.
- Zhang, H., Yu, Y., Jiao, J., Xing, E., Ghaoui, L.E. and Jordan, M. (2019). Theoretically principled trade-off between robustness and accuracy, Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 7472–7482.
- Zhang, J., Qian, W., Nie, R., Cao, J. and Xu, D. (2023). Generate adversarial examples by adaptive moment iterative fast gradient sign method, Applied Intelligence 53(1): 1101–1114.
- Zhang, X. (2016). Empirical risk minimization, in C. Sammut and G.I. Webb (Eds), Encyclopedia of Machine Learning and Data Mining, Springer, Berlin/Heidelberg, pp. 392–393.
- Zhao, K., Chen, X., Huang, W., Ding, L., Kong, X. and Zhang, F. (2024). Ensemble adversarial defense via integration of multiple dispersed low curvature models, CoRR: abs/2403.16405.
Language: English
Page range: 425 - 438
Submitted on: Feb 9, 2024
Accepted on: May 14, 2024
Published on: Oct 1, 2024
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Related subjects:
© 2024 Yaguan Qian, Liangjian Zhang, Yuqi Wang, Boyuan Ji, Tengteng Yao, Bin Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.