Improving robustness with image filtering
References
[1]
Szegedy C., Zaremba W., Sutskever I., Bruna J., Erhan D., Goodfellow I., Fergus R., Intriguing properties of neural networks, 2013, arXiv:1312.6199.
[2]
Athalye A., Carlini N., Wagner D., Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples, 2018, arXiv:1802.00420.
[3]
Tramer F., Carlini N., Brendel W., Madry A., On adaptive attacks to adversarial example defenses, 2020, arXiv preprint arXiv:2002.08347.
[4]
Guo C., Rana M., Cisse M., Van Der Maaten L., Countering adversarial images using input transformations, 2017, arXiv preprint arXiv:1711.00117.
[5]
Madry A., Makelov A., Schmidt L., Tsipras D., Vladu A., Towards deep learning models resistant to adversarial attacks, 2017, arXiv:1706.06083.
[6]
Goodfellow I.J., Shlens J., Szegedy C., Explaining and harnessing adversarial examples, 2014, arXiv:1412.6572.
[7]
Kurakin A., Goodfellow I., Bengio S., Adversarial machine learning at scale, 2016, arXiv:1611.01236.
[8]
Kannan H., Kurakin A., Goodfellow I., Adversarial logit pairing, 2018, arXiv preprint arXiv:1803.06373.
[9]
Moosavi-Dezfooli S.-M., Fawzi A., Uesato J., Frossard P., Robustness via curvature regularization, and vice versa, 2018, URL https://arxiv.org/pdf/1811.09716.
[10]
Shaham U., Yamada Y., Negahban S., Understanding adversarial training: Increasing local stability of neural nets through robust optimization, 2015, arXiv preprint arXiv:1511.05432.
[11]
Carmon Y., Raghunathan A., Schmidt L., Liang P., Duchi J.C., Unlabeled data improves adversarial robustness, 2019, arXiv preprint arXiv:1905.13736.
[12]
Gowal S., Rebuffi S.-A., Wiles O., Stimberg F., Calian D.A., Mann T.A., Improving robustness using generated data, Adv. Neural Inf. Process. Syst. 34 (2021) 4218–4233.
[13]
Rebuffi S.-A., Gowal S., Calian D.A., Stimberg F., Wiles O., Mann T.A., Data augmentation can improve robustness, Adv. Neural Inf. Process. Syst. 34 (2021) 29935–29948.
[14]
Qin C., Martens J., Gowal S., Krishnan D., Dvijotham K., Fawzi A., De S., Stanforth R., Kohli P., Adversarial robustness through local linearization, 2019, arXiv preprint arXiv:1907.02610.
[15]
Cisse M., Bojanowski P., Grave E., Dauphin Y., Usunier N., Parseval networks: Improving robustness to adversarial examples, in: International Conference on Machine Learning, PMLR, 2017, pp. 854–863.
[16]
Papernot N., McDaniel P., Wu X., Jha S., Swami A., Distillation as a defense to adversarial perturbations against deep neural networks, in: 2016 IEEE Symposium on Security and Privacy, SP, IEEE, 2016, pp. 582–597.
[17]
Pang T., Xu K., Du C., Chen N., Zhu J., Improving adversarial robustness via promoting ensemble diversity, in: International Conference on Machine Learning, PMLR, 2019, pp. 4970–4979.
[18]
Bai Y., Zeng Y., Jiang Y., Xia S.-T., Ma X., Wang Y., Improving adversarial robustness via channel-wise activation suppressing, 2021, arXiv preprint arXiv:2103.08307.
[19]
C. Xie, Y. Wu, L.v.d. Maaten, A.L. Yuille, K. He, Feature denoising for improving adversarial robustness, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 501–509.
[20]
C. Shi, C. Holtz, G. Mishne, Online adversarial purification based on self-supervised learning, in: International Conference on Learning Representations, 2020.
[21]
Guo Y., Stutz D., Schiele B., Improving robustness by enhancing weak subnets, in: European Conference on Computer Vision, Springer, 2022, pp. 320–338.
[22]
Dziugaite G.K., Ghahramani Z., Roy D.M., A study of the effect of jpg compression on adversarial images, 2016, arXiv preprint arXiv:1608.00853.
[23]
Lu J., Sibai H., Fabry E., Forsyth D., No need to worry about adversarial examples in object detection in autonomous vehicles, 2017, arXiv preprint arXiv:1707.03501.
[24]
Tsipras D., Santurkar S., Engstrom L., Turner A., Madry A., Robustness may be at odds with accuracy, in: International Conference on Learning Representations, 2019, URL https://openreview.net/forum?id=SyxAb30cY7.
[25]
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, 2019, pp. 125–136.
[26]
Sharma G., Wu W., Dalal E.N., The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations, Color Res. Appl. 30 (1) (2005) 21–30.
[27]
Stutz D., Hermans A., Leibe B., Superpixels: An evaluation of the state-of-the-art, Comput. Vis. Image Underst. 166 (2018) 1–27.
[28]
Krizhevsky A., Nair V., Hinton G., Cifar-10 and cifar-100 datasets, 2009, URL https://www.cs.toronto.edu/kriz/cifar.html 6.
[29]
Deng J., Dong W., Socher R., Li L.-J., Li K., Fei-Fei L., Imagenet: A large-scale hierarchical image database, in: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 248–255.
[30]
He K., Zhang X., Ren S., Sun J., Identity mappings in deep residual networks, 2016, arXiv:1603.05027.
[31]
Salman H., Ilyas A., Engstrom L., Kapoor A., Madry A., Do adversarially robust imagenet models transfer better?, Adv. Neural Inf. Process. Syst. 33 (2020) 3533–3545.
[32]
Achanta R., Shaji A., Smith K., Lucchi A., Fua P., Süsstrunk S., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Pattern Anal. Mach. Intell. 34 (11) (2012) 2274–2282.
[33]
Salman H., Ilyas A., Engstrom L., Vemprala S., Madry A., Kapoor A., Unadversarial examples: Designing objects for robust vision, 2020, arXiv preprint arXiv:2012.12235.
[34]
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., et al., An image is worth 16 × 16 words: Transformers for image recognition at scale, 2020, arXiv preprint arXiv:2010.11929.
[35]
Playne D.P., Hawick K., A new algorithm for parallel connected-component labelling on GPUs, IEEE Trans. Parallel Distrib. Syst. 29 (6) (2018) 1217–1230.
Index Terms
- Improving robustness with image filtering
Index terms have been assigned to the content through auto-classification.
Recommendations
Improving adversarial robustness of deep neural networks via adaptive margin evolution
Highlights- A poof of the existence of an optimal state for adversarial training.
- Adaptive ...
AbstractAdversarial training is the most popular and general strategy to improve Deep Neural Network (DNN) robustness against adversarial noises. Many adversarial training methods have been proposed in the past few years. However, most of ...
ATGAN: Adversarial training-based GAN for improving adversarial robustness generalization on image classification
AbstractDeep neural networks are vulnerable to adversarial examples, which are well-designed examples aiming to cause models to produce wrong outputs with high confidence. Although adversarial training is by so far the only effective adversarial defense ...
Layer-wise regularized adversarial training using layers sustainability analysis framework
Highlights- The layer sustainability analysis (LSA) framework is introduced to evaluate the behavior of layer-level representations of DNNs in dealing with network input ...
AbstractDeep neural network models are used today in various applications of artificial intelligence, the strengthening of which, in the face of adversarial attacks is of particular importance. An appropriate solution to adversarial attacks is ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier B.V.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 25 September 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in