Foreword to the Special Issue on Advances in Secure AI: Technology and Applications
Source abstract
Abstract
I am pleased to introduce the Special Issue on “Advances in Secure AI: Technology and Applications” [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Adversarial Robustness in Machine Learning · Advanced Malware Detection Techniques · Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Cybersecurity, Korea University, Seoul 02841, Korea
Funding
Korea University Grant
References (18)
Show source-provided references
1. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R. (2014, January 14–16). Intriguing properties of neural networks. Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada.
2. Goodfellow, I.J., Shlens, J., and Szegedy, C. (2015, January 7–9). Explaining and Harnessing Adversarial Examples. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA.
3. Moosavi-Dezfooli, S.M., Fawzi, A., and Frossard, P. (2016, January 27–30). DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.282 ↗
4. Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., and Swami, A. (2017, January 2–6). Practical Black-Box Attacks against Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, Abu Dhabi, United Arab Emirates.10.1145/3052973.3053009 ↗
5. Carlini, N., and Wagner, D. Towards evaluating the robustness of neural networks. Proceedings of the In Proceedings of the 2017 IEEE Symposium on Security and Privacy, San Jose, CA, USA, 22–26 May 2017.10.1109/SP.2017.49 ↗
6. Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., and Frossard, P. (2017, January 21–26). Universal Adversarial Perturbations. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.17 ↗
7. Tramèr, F., Zhang, F., Juels, A., Reiter, M.K., and Ristenpart, T. (2016, January 10–12). Stealing Machine Learning Models via Prediction APIs. Proceedings of the 25th USENIX Conference on Security Symposium, Austin, TX, USA.
8. Orekondy, T., Schiele, B., and Fritz, M. (2019, January 15–20). Knockoff Nets: Stealing Functionality of Black-Box Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00509 ↗
9. Chen, X., Liu, C., Li, B., Lu, K., and Song, D. (2017). Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning. arXiv.
10. Muñoz González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E.C., and Roli, F. (2017, January 3). Towards Poisoning of Deep Learning Algorithms with Back-Gradient Optimization. Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, Dallas, TX, USA.10.1145/3128572.3140451 ↗
11. Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M.K. (2016, January 24–28). Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.10.1145/2976749.2978392 ↗
12. Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D. (2018, January 18–23). Robust Physical-World Attacks on Deep Learning Visual Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00175 ↗
13. Wolf, S., Hu, H., Cooley, R., and Borowczak, M. (2021, January 7–9). Stealing Machine Learning Parameters via Side Channel Power Attacks. Proceedings of the 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Tampa, FL, USA.10.1109/ISVLSI51109.2021.00052 ↗
14. Ben-Or, M., Goldwasser, S., and Wigderson, A. (1988, January 16–18). Completeness Theorems for Non-Cryptographic Fault-Tolerant Distributed Computation. Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing, New York, NY, USA.10.1145/62212.62213 ↗
15. Konečný, J., McMahan, B., and Ramage, D. (2015). Federated Optimization:Distributed Optimization Beyond the Datacenter. arXiv.
16. Gentry, C. (June, January 31). Fully Homomorphic Encryption Using Ideal Lattices. Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, Bethesda, MD, USA. STOC ’09.
17. Bugliesi, M., Preneel, B., Sassone, V., and Wegener, I. (2006). Differential Privacy. Proceedings of the Automata, Languages and Programming, Springer.10.1007/11786986 ↗
18. Gunning, D. (2016). Explainable Artificial Intelligence (XAI), DARPA-BAA-16-53.