| Draw it as shown: Behavioral pattern lock for mobile user authentication Y Ku, LH Park, S Shin, T Kwon IEEE Access 7, 69363-69378, 2019 | 48 | 2019 |
| Birds of a Feature: Intrafamily Clustering for Version Identification of Packed Malware LH Park, J Yu, HK Kang, T Lee, T Kwon IEEE Systems Journal 14 (3), 4545-4556, 2020 | 18 | 2020 |
| Membership inference attacks with token-level deduplication on korean language models MG Oh, LH Park, J Kim, J Park, T Kwon IEEE Access 11, 10207-10217, 2023 | 17 | 2023 |
| GradFuzz: Fuzzing deep neural networks with gradient vector coverage for adversarial examples LH Park, S Chung, J Kim, T Kwon Neurocomputing 522, 165-180, 2023 | 7 | 2023 |
| Adversarial feature alignment: Balancing robustness and accuracy in deep learning via adversarial training LH Park, J Kim, MG Oh, J Park, T Kwon Proceedings of the 2024 Workshop on Artificial Intelligence and Security …, 2024 | 4 | 2024 |
| Analysis of Deep Learning Model Vulnerability According to Input Mutation J Kim, LH Park, T Kwon Journal of the Korea Institute of Information Security & Cryptology 31 (1 …, 2021 | 4 | 2021 |
| Coexistence of Deepfake Defenses: Addressing the Poisoning Challenge J Park, LH Park, HE Ahn, T Kwon IEEE Access 12, 11674-11687, 2024 | 3 | 2024 |
| Mixed and constrained input mutation for effective fuzzing of deep learning systems LH Park, J Kim, J Park, T Kwon Information Sciences 614, 497-517, 2022 | 3 | 2022 |
| Poster: Effective layers in coverage metrics for deep neural networks LH Park, S Oh, J Kim, S Chung, T Kwon Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications …, 2019 | 3 | 2019 |
| A guided approach to behavioral authentication Y Ku, LH Park, S Shin, T Kwon Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 3 | 2018 |
| On the Correlation Between Deepfake Detection Performance and Image Quality Metrics H Kim, J Lee, LH Park, T Kwon Proceedings of the 3rd ACM Workshop on the Security Implications of …, 2024 | 2 | 2024 |
| Poster: adversarial defense with deep learning coverage on magnet's purification LH Park, J Park, S Chung, J Kim, MG Oh, T Kwon Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022 | 2 | 2022 |
| Towards constructing consistent pattern strength meters with user’s visual perception LH Park, E Hwang, D Lee, T Kwon International Conference on Information Security and Cryptology, 81-99, 2022 | 1 | 2022 |
| On Membership Inference Attacks to Generative Language Models Across Language Domains MG Oh, LH Park, J Kim, J Park, T Kwon International Conference on Information Security Applications, 143-155, 2022 | 1 | 2022 |
| Development Research of An Efficient Malware Classification System Using Hybrid Features And Machine Learning JB Yu, SJ Oh, LH Park, TK Kwon Journal of the Korea Institute of Information Security & Cryptology 28 (5 …, 2018 | 1 | 2018 |
| Enhancing Robustness in NIDS via Coverage Guided Fuzzing and Adversarial Training JY Rhee, LH Park, T Kwon International Conference on Information Security Applications, 56-68, 2024 | | 2024 |
| Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled Memberships MG Oh, HE Ahn, LH Park, T Kwon arXiv preprint arXiv:2402.12189, 2024 | | 2024 |
| Reverse-Update Adversarial Data for Enhancing Adversarial Attack and Adversarial Training Performance JY Rhee, W Cho, LH Park, T Kwon Journal of the Korea Institute of Information Security & Cryptology 34 (5 …, 2024 | | 2024 |
| On the Performance Biases Arising from Inconsistencies in Evaluation Methodologies of Deepfake Detection Models H Kim, HE Ahn, LH Park, T Kwon Journal of the Korea Institute of Information Security & Cryptology 34 (5 …, 2024 | | 2024 |
| Robust Training for Deepfake Detection Models Against Disruption-Induced Data Poisoning J Park, HE Ahn, LH Park, T Kwon International Conference on Information Security Applications, 175-187, 2023 | | 2023 |
