Author:
Li Shasha,Zhu Shitong,Paul Sudipta,Roy-Chowdhury Amit,Song Chengyu,Krishnamurthy Srikanth,Swami Ananthram,Chan Kevin S.
Publisher
Springer International Publishing
Reference49 articles.
1. Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: Synthesizing robust adversarial examples. arXiv preprint arXiv:1707.07397 (2017)
2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
3. Lecture Notes in Computer Science;JH Bappy,2016
4. Barnea, E., Ben-Shahar, O.: Exploring the bounds of the utility of context for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7412–7420 (2019)
5. Carlini, N., Wagner, D.: Adversarial examples are not easily detected: bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 3–14 (2017)
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Towards Retentive Proactive Defense Against DeepFakes;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024
2. PRAT: PRofiling Adversarial aTtacks;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02
3. An Approach to Effective Processing of Information from Smart Home Sensors using Machine Learning Methods;2023 V International Conference on Control in Technical Systems (CTS);2023-09-21
4. A review of generative and non-generative adversarial attack on context-rich images;Engineering Applications of Artificial Intelligence;2023-09
5. Defeating DeepFakes via Adversarial Visual Reconstruction;Proceedings of the 30th ACM International Conference on Multimedia;2022-10-10