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참고문헌 (50)

1. Liu, Weibo, et al. "A survey of deep neural network architectures and their applications." Neurocomputing 234 (2017): 11-26. 

   상세보기

2. Parvathy, Velmurugan Subbiah, Sivakumar Pothiraj, and Jenyfal Sampson. "Optimal Deep Neural Network model based multimodality fused medical image classification." Physical Communication 41 (2020): 101119. 

   상세보기

3. Jahangir, Rashid, et al. "Text-independent speaker identification through feature fusion and deep neural network." IEEE Access 8 (2020): 32187-32202. 

   상세보기

4. Xue, Mingfu, et al. "Machine learning security: Threats, countermeasures, and evaluations." IEEE Access 8 (2020): 74720-74742. 

   상세보기

5. Yang, Chaofei, et al. "Generative poisoning attack method against neural networks." arXiv preprint arXiv:1703.01340 (2017). 

6. Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Multi-targeted backdoor: Indentifying backdoor attack for multiple deep neural networks." IEICE Transactions on Information and Systems 103.4 (2020): 883-887. 

7. Akhtar, Naveed, and Ajmal Mian. "Threat of adversarial attacks on deep learning in computer vision: A survey." Ieee Access 6 (2018): 14410-14430. 

   상세보기

8. Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint arXiv:1312.6199 (2013). 

9. Carlini, Nicholas, et al. "Provably minimally-distorted adversarial examples." arXiv preprint arXiv:1709.10207 (2017). 

10. Athalye, Anish, and Nicholas Carlini. "On the robustness of the cvpr 2018 white-box adversarial example defenses." arXiv preprint arXiv:1804.03286 (2018). 

11. Jiang, Linxi, et al. "Black-box adversarial attacks on video recognition models." Proceedings of the 27th ACM International Conference on Multimedia. 2019. 

12. Zhao, Yuhang, et al. "An Universal Perturbation Generator for Black-Box Attacks Against Object Detectors." International Conference on Smart Computing and Communication. Springer, Cham, 2019. 

13. Huang, Qian, et al. "Enhancing adversarial example transferability with an intermediate level attack." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. 

14. Kwon, Hyun, et al. "Advanced ensemble adversarial example on unknown deep neural network classifiers." IEICE TRANSACTIONS on Information and Systems 101.10 (2018): 2485-2500. 

    상세보기

15. Hang, Jie, et al. "Ensemble adversarial black-box attacks against deep learning systems." Pattern Recognition 101 (2020): 107184. 

    상세보기

16. Papernot, Nicolas, et al. "Practical black-box attacks against machine learning." Proceedings of the 2017 ACM on Asia conference on computer and communications security. 2017. 

17. Kwon, Hyun, et al. "Multi-targeted adversarial example in evasion attack on deep neural network." IEEE Access 6 (2018): 46084-46096. 

    상세보기

18. Carlini, Nicholas, and David Wagner. "Audio adversarial examples: Targeted attacks on speech-to-text." 2018 IEEE Security and Privacy Workshops (SPW). IEEE, 2018. 

19. Wu, Aming, et al. "Untargeted adversarial attack via expanding the semantic gap." 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. 

20. Kwon, Hyun, et al. "Random untargeted adversarial example on deep neural network." Symmetry 10.12 (2018): 738. 

    상세보기

21. Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. CoRR, abs/1412.6572, 2014 

22. Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, and Pascal Frossard. "Deepfool: a simple and accurate method to fool deep neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. 

23. Papernot, Nicolas, et al. "The limitations of deep learning in adversarial settings." 2016 IEEE European symposium on security and privacy (EuroS&P). IEEE, 2016. 

24. Carlini, Nicholas, and David Wagner. "Towards evaluating the robustness of neural networks." 2017 ieee symposium on security and privacy (sp). IEEE, 2017. 

25. Su, Jiawei, Danilo Vasconcellos Vargas, and Kouichi Sakurai. "One pixel attack for fooling deep neural networks." IEEE Transactions on Evolutionary Computation 23.5 (2019): 828-841. 

    상세보기

26. He, Warren, Bo Li, and Dawn Song. "Decision boundary analysis of adversarial examples." International Conference on Learning Representations. 2018. 

27. Athalye, Anish, Nicholas Carlini, and David Wagner. "Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples." International Conference on Machine Learning. PMLR, 2018. 

28. Tramer, Florian, et al. "On adaptive attacks to adversarial example defenses." arXiv preprint arXiv:2002.08347 (2020). 

29. Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Acoustic-decoy: Detection of adversarial examples through audio modification on speech recognition system." Neurocomputing 417 (2020): 357-370. 

    상세보기

30. Kwon, Hyun, and Jun Lee. "AdvGuard: Fortifying Deep Neural Networks against Optimized Adversarial Example Attack." IEEE Access (2020). 

31. Kwon, Hyun, et al. "Classification score approach for detecting adversarial example in deep neural network." Multimedia Tools and Applications (2020): 1-22. 

32. Liang, Bin, et al. "Detecting adversarial image examples in deep neural networks with adaptive noise reduction." IEEE Transactions on Dependable and Secure Computing (2018). 

33. Papernot, Nicolas, et al. "Distillation as a defense to adversarial perturbations against deep neural networks." 2016 IEEE symposium on security and privacy (SP). IEEE, 2016. 

34. Samangouei, Pouya, Maya Kabkab, and Rama Chellappa. "Defense-gan: Protecting classifiers against adversarial attacks using generative models." arXiv preprint arXiv:1805.06605 (2018). 

35. Meng, Dongyu, and Hao Chen. "Magnet: a two-pronged defense against adversarial examples." Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 2017. 

36. Xu, Weilin, David Evans, and Yanjun Qi. "Feature squeezing: Detecting adversarial examples in deep neural networks." arXiv preprint arXiv:1704.01155 (2017). 

37. Tramer, Florian, et al. "Ensemble adversarial training: Attacks and defenses." arXiv preprint arXiv:1705.07204 (2017). 

38. Kwon, Hyun, and Jun Lee. "Diversity Adversarial Training against Adversarial Attack on Deep Neural Networks." Symmetry 13.3 (2021): 428. 

    상세보기

39. Shumailov, Ilia, et al. "Towards certifiable adversarial sample detection." Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security. 2020. 

40. Carlini, Nicholas, and David Wagner. "Adversarial examples are not easily detected: Bypassing ten detection methods." Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. 2017. 

41. Kwon, Hyun, et al. "Selective audio adversarial example in evasion attack on speech recognition system." IEEE Transactions on Information Forensics and Security 15 (2019): 526-538. 

    상세보기

42. Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods." IEICE TRANSACTIONS on Information and Systems 103.4 (2020): 879-882. 

43. Carlini, Nicholas, and David Wagner. "Audio adversarial examples: Targeted attacks on speech-to-text." 2018 IEEE Security and Privacy Workshops (SPW). IEEE, 2018. 

44. Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "POSTER: Detecting audio adversarial example through audio modification." Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 2019. 

45. Zhang, Guoming, et al. "Dolphinattack: Inaudible voice commands." Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017. 

46. Carlini, Nicholas, et al. "Hidden voice commands." 25th {USENIX} Security Symposium ({USENIX} Security 16). 2016. 

47. Garg, Siddhant, and Goutham Ramakrishnan. "Bae: Bert-based adversarial examples for text classification." arXiv preprint arXiv:2004.01970 (2020). 

48. Jin, Di, et al. "Is bert really robust? a strong baseline for natural language attack on text classification and entailment." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 05. 2020. 

49. Finlayson, Samuel G., et al. "Adversarial attacks against medical deep learning systems." arXiv preprint arXiv:1804.05296 (2018). 

50. Ozbulak, Utku, Arnout Van Messem, and Wesley De Neve. "Impact of adversarial examples on deep learning models for biomedical image segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.