Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 7 | |
| Section | Machine Learning Applications in Vision, Security, and Healthcare | |
| DOI | https://doi.org/10.1051/itmconf/20257802003 | |
| Published online | 08 September 2025 | |
- Akhtar, N., Mian, A.: "Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey," arXiv.org, 2018. [Google Scholar]
- Xu, Y., Raja, K., Ramachandra, R., Busch, C.: "Adversarial Attacks on Face Recognition Systems," in Advances in Computer Vision and Pattern Recognition, 2022, pp. 139–161. [Google Scholar]
- Xu, K., Zhang, G., Liu, S., Fan, Q., Sun, M., Chen, H., Chen, P., Wang, Y., Lin, X.: "Adversarial T-Shirt! Evading Person Detectors in a Physical World," in Lecture Notes in Computer Science, 2020, pp. 665–681. [Google Scholar]
- Deng, J., Guo, J., Xue, N., Zafeiriou, S.: "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4690–4699. [Google Scholar]
- Goodfellow, I. J., Shlens, J., Szegedy, C.: "Explaining and Harnessing Adversarial Examples," arXiv.org, 2014. [Google Scholar]
- Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: "Towards Deep Learning Models Resistant to Adversarial Attacks," arXiv.org, 2017. [Google Scholar]
- Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M. K.: "Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition," in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16), Association for Computing Machinery, New York, NY, USA, 2016, pp. 1528–1540. [Google Scholar]
- Huang, G. B., Mattar, M., Berg, T., Learned-Miller, E.: "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments," 2008. [Google Scholar]
- Cao, Q., Shen, L., Xie, W., Parkhi, O. M., Zisserman, A.: "VGGFace2: A Dataset for Recognising Faces Across Pose and Age," in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018, pp. 67–74, IEEE. [Google Scholar]
- Song, Y., Shu, R., Kushman, N., Ermon, S.: "Constructing Unrestricted Adversarial Examples with Generative Models," arXiv.org, 2018. [Google Scholar]
- Kim, T., Yu, Y., Ro, Y. M.: "Defending Physical Adversarial Attack on Object Detection via Adversarial Patch-Feature Energy," in Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 1905–1913. [Google Scholar]
- Tabassi, E.: "A Taxonomy and Terminology of Adversarial Machine Learning," 2023. [Google Scholar]
- Vakhshiteh, F., Nickabadi, A., Ramachandra, R.: "Adversarial Attacks Against Face Recognition: A Comprehensive Study," IEEE Access, 2021, 9, pp. 92735–92756. [Google Scholar]
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