Publication · JMLR 2025
Regularizing Hard Examples Improves Adversarial Robustness
Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon · Journal of Machine Learning Research · 2025
Adversarial robustness method that regularizes hard examples to improve robust generalization.
Publication · ICLR 2024
DAFA: Distance-Aware Fair Adversarial Training
Hyungyu Lee, Saehyung Lee, Hyemi Jang, Junsung Park, Ho Bae, Sungroh Yoon · ICLR · Vienna, May 2024
Adversarial training method that enforces fairness across subgroups via distance-aware margin adjustment.
Publication · Sensors 2024
Evaluation of Malware Classification Models for Heterogeneous Data
Ho Bae · Sensors (MDPI) · 2024
Study of malware-classifier explainability on heterogeneous data. Existing explanations fall short, and high accuracy can give a misleading sense of security.
Publication · ESORICS 2024
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification
Yungi Cho, Woorim Han, Miseon Yu, Younghan Lee, Ho Bae, Yunheung Paek · ESORICS · 2024
First backdoor defense specialized for Vertical Federated Learning. It identifies and purifies backdoor-triggered embeddings at inference.
Publication · BIBM 2023
Privacy-Preserving Publishing of Individual-Level Medical Data for Cloud Services
Ho Bae, Heonseok Ha, Siwon Kim · IEEE BIBM · Istanbul, Dec 2023
Formal privacy-preserving framework for publishing patient-level medical records to cloud services, with emphasis on utility preservation under strict privacy constraints.
Publication · ESORICS 2023
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
Younghan Lee, Yungi Cho, Woorim Han, Ho Bae, Yunheung Paek · ESORICS · 2023
Byzantine-robust federated learning that detects malicious clients via an ensemble of contrastive models, strong under non-IID data.
Publication · RAID 2023
Exploring Clustered Federated Learning's Vulnerability against Property Inference Attack
Hyunjun Kim, Yungi Cho, Younghan Lee, Ho Bae, Yunheung Paek · RAID · 2023
Reveals property-inference privacy risks in clustered federated learning.
Publication · IEEE/ACM TCBB 2022
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks
Ho Bae, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon · IEEE/ACM Transactions on Computational Biology and Bioinformatics · 2022
Deep-learning analysis of malicious DNA sequences for security and privacy in genomic data.
Publication · BMVC 2022
MPGAN: Membership Privacy-Preserving GAN
Heonseok Ha, Uiwon Hwang, Jaehee Jang, Ho Bae, Sungroh Yoon · BMVC · London, Nov 2022
GAN training method that prevents membership inference attacks on generated data, providing formal privacy guarantees for synthetic outputs.
Publication · ACM AsiaCCS 2022
Membership Feature Disentanglement Network
Heonseok Ha, J Jang, Y Jeong, S Yoon · ACM Asia Conference on Computer and Communications Security · 2022
Network architecture that disentangles membership-sensitive features from model representations, reducing exposure to membership inference attacks.
Publication · IEEE Access 2021
Gradient Masking of Label Smoothing in Adversarial Robustness
Hyungyu Lee, Ho Bae, Sungroh Yoon · IEEE Access · 2021
Analysis of how label smoothing induces gradient masking, a false sense of robustness that does not transfer to true adversarial settings.
Publication · IEEE TAI 2021
Learn2Evade: Learning-based Generative Model for Evading PDF Malware Classifiers
Ho Bae, Younghan Lee, Yohan Kim, Uiwon Hwang, Sungroh Yoon, Yunheung Paek · IEEE Transactions on Artificial Intelligence · Aug 2021
Adversarial generative modeling of malware evasion: learning to produce feature-space perturbations that bypass PDF malware classifiers while preserving functionality.
Publication · IEEE Access 2020
Anomaly Detection by Learning Dynamics From a Graph
Jaekoo Lee, Ho Bae, Sungroh Yoon · IEEE Access · 2020
Graph-based anomaly detection that learns system dynamics to flag abnormal behavior.
Publication · PSB 2020
AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data
Ho Bae, Dahuin Jung, Hyun-Soo Choi, Sungroh Yoon · Pacific Symposium on Biocomputing · Hawaii, Jan 2020
GAN-based anonymization of private medical datasets while preserving statistical utility for downstream analysis.
Publication · PSB 2019
DNA Steganalysis Using Deep Recurrent Neural Networks
Ho Bae, Byunghan Lee, Sunyoung Kwon, Sungroh Yoon · Pacific Symposium on Biocomputing · Hawaii, Jan 2019
Deep recurrent-network method for detecting hidden messages embedded in DNA sequences (steganalysis), applied to genomic data.
Preprint · arXiv 2018
Security and Privacy Issues in Deep Learning
Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Sungroh Yoon · arXiv:1807.11655 · 2018
Comprehensive survey of attack surfaces and defenses in deep learning systems, covering adversarial examples, model extraction, and data poisoning.
