SENA: Preserving Social Structure for Network Embedding. - Sanghyun Hong 001; Tanmoy Chakraborty 002; Sungjin Ahn; Ghaith Husari; Noseong Park (HT, 2017)

Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning. - Rock Stevens; Octavian Suciu; Andrew Ruef; Sanghyun Hong 001; Michael W. Hicks; Tudor Dumitras (CoRR, 2017)

PAGE: Answering Graph Pattern Queries via Knowledge Graph Embedding. - Sanghyun Hong 001; Noseong Park; Tanmoy Chakraborty 002; Hyunjoong Kang; Soonhyun Kwon (BigData Congress, 2018)

Go Serverless: Securing Cloud via Serverless Design Patterns. - Sanghyun Hong 001; Abhinav Srivastava; William Shambrook; Tudor Dumitras (HotCloud, 2018)

On Integrating Knowledge Graph Embedding into SPARQL Query Processing. - Hyunjoong Kang; Sanghyun Hong 001; Kookjin Lee; Noseong Park; Soonhyun Kwon (ICWS, 2018)

Peek-a-boo: Inferring program behaviors in a virtualized infrastructure without introspection. - Sanghyun Hong 001; Alina Nicolae; Abhinav Srivastava; Tudor Dumitras (Comput. Secur., 2018)

Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks. - Sanghyun Hong 001; Michael Davinroy; Yigitcan Kaya; Stuart Nevans Locke; Ian Rackow; Kevin Kulda; Dana Dachman-Soled; Tudor Dumitras (CoRR, 2018)

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking. - Yigitcan Kaya; Sanghyun Hong 001; Tudor Dumitras (ICML, 2019)

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks. - Sanghyun Hong 001; Pietro Frigo; Yigitcan Kaya; Cristiano Giuffrida; Tudor Dumitras (USENIX Security Symposium, 2019)

Poster: On the Feasibility of Training Neural Networks with Visibly Watermarked Dataset. - Sanghyun Hong 001; Tae-Hoon Kim 003; Tudor Dumitras; Jonghyun Choi (CoRR, 2019)

How to 0wn the NAS in Your Spare Time. - Sanghyun Hong 001; Michael Davinroy; Yigitcan Kaya; Dana Dachman-Soled; Tudor Dumitras (ICLR, 2020)

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. - Sanghyun Hong 001; Varun Chandrasekaran; Yigitcan Kaya; Tudor Dumitras; Nicolas Papernot (CoRR, 2020)

On the Effectiveness of Regularization Against Membership Inference Attacks. - Yigitcan Kaya; Sanghyun Hong 001; Tudor Dumitras (CoRR, 2020)

Certified Malware in South Korea: A Localized Study of Breaches of Trust in Code-Signing PKI Ecosystem. - Bum Jun Kwon; Sanghyun Hong 001; Yuseok Jeon; Doowon Kim (ICICS (1), 2021)

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference. - Sanghyun Hong 001; Yigitcan Kaya; Ionut-Vlad Modoranu; Tudor Dumitras (ICLR, 2021)

Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. - Sanghyun Hong 001; Michael-Andrei Panaitescu-Liess; Yigitcan Kaya; Tudor Dumitras (NeurIPS, 2021)

Building Secure and Reliable Deep Learning Systems from a Systems Security Perspective. - Sanghyun Hong 001 (2021)

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. - Florian Tramèr; Reza Shokri; Ayrton San Joaquin; Hoang Le; Matthew Jagielski; Sanghyun Hong 001; Nicholas Carlini (CCS, 2022)

Data Poisoning Won't Save You From Facial Recognition. - Evani Radiya-Dixit; Sanghyun Hong 001; Nicholas Carlini; Florian Tramèr (ICLR, 2022)

Improving cross-platform binary analysis using representation learning via graph alignment. - Geunwoo Kim; Sanghyun Hong 001; Michael Franz; Dokyung Song (ISSTA, 2022)

Handcrafted Backdoors in Deep Neural Networks. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (NeurIPS, 2022)

AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation. - Suneghyeon Cho; Sanghyun Hong 001; Kookjin Lee; Noseong Park (CoRR, 2022)

Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting. - Fan Wu; Sanghyun Hong 001; Donsub Rim; Noseong Park; Kookjin Lee (CoRR, 2022)

Time Series Forecasting with Hypernetworks Generating Parameters in Advance. - Jaehoon Lee 002; Chan Kim; Gyumin Lee; Haksoo Lim; Jeongwhan Choi 002; Kookjin Lee; Dongeun Lee 001; Sanghyun Hong 001; Noseong Park (CoRR, 2022)

Perceptual Pat: A Virtual Human Visual System for Iterative Visualization Design. - Sungbok Shin; Sanghyun Hong 001; Niklas Elmqvist (CHI, 2023)

Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator. - Sicheng Zhu; Bang An 001; Furong Huang; Sanghyun Hong 001 (ICML, 2023)

BERT Lost Patience Won't Be Robust to Adversarial Slowdown. - Zachary Coalson; Gabriel Ritter; Rakesh Bobba; Sanghyun Hong 001 (NeurIPS, 2023)

Publishing Efficient On-device Models Increases Adversarial Vulnerability. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (SaTML, 2023)

Perceptual Pat: A Virtual Human System for Iterative Visualization Design. - Sungbok Shin; Sanghyun Hong 001; Niklas Elmqvist (CoRR, 2023)

A Scanner Deeply: Predicting Gaze Heatmaps on Visualizations Using Crowdsourced Eye Movement Data. - Sungbok Shin; Sunghyo Chung; Sanghyun Hong 001; Niklas Elmqvist (IEEE Trans. Vis. Comput. Graph., 2023)

Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations. - Woojin Cho; Seunghyeon Cho; Hyundong Jin; Jinsung Jeon; Kookjin Lee; Sanghyun Hong 001; Dongeun Lee 001; Jonghyun Choi; Noseong Park (AAAI, 2024)

You Only Perturb Once: Bypassing (Robust) Ad-Blockers Using Universal Adversarial Perturbations. - Dongwon Shin; Suyoung Lee; Sanghyun Hong 001; Sooel Son (ACSAC, 2024)

Identifying Contemporaneous and Lagged Dependence Structures by Promoting Sparsity in Continuous-time Neural Networks. - Fan Wu; Woojin Cho; David Korotky; Sanghyun Hong 001; Donsub Rim; Noseong Park; Kookjin Lee (CIKM, 2024)

When Do "More Contexts" Help with Sarcasm Recognition? - Ojas Nimase; Sanghyun Hong 001 (LREC/COLING, 2024)

Message from the DSML Workshop Chairs; DSN-W 2024. - Sanghyun Hong 001; Bo Fang (DSN-W, 2024)

PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images. - Jinsung Jeon; Hyundong Jin; Jonghyun Choi; Sanghyun Hong 001; Dongeun Lee 001; Kookjin Lee; Noseong Park (ICLR, 2024)

LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions. - Victor Agostinelli; Sanghyun Hong 001; Lizhong Chen (ICML, 2024)

Parameterized Physics-informed Neural Networks for Parameterized PDEs. - Woojin Cho; Minju Jo; Haksoo Lim; Kookjin Lee; Dongeun Lee 001; Sanghyun Hong 001; Noseong Park (ICML, 2024)

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. - Yuxin Wen; Leo Marchyok; Sanghyun Hong 001; Jonas Geiping; Tom Goldstein; Nicholas Carlini (NeurIPS, 2024)

Diffusion Denoising as a Certified Defense against Clean-label Poisoning. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (CoRR, 2024)

Hard Work Does Not Always Pay Off: Poisoning Attacks on Neural Architecture Search. - Zachary Coalson; Huazheng Wang; Qingyun Wu; Sanghyun Hong 001 (CoRR, 2024)

Visualizationary: Automating Design Feedback for Visualization Designers using LLMs. - Sungbok Shin; Sanghyun Hong 001; Niklas Elmqvist (CoRR, 2024)

You Never Know: Quantization Induces Inconsistent Biases in Vision-Language Foundation Models. - Eric Slyman; Anirudh Kanneganti; Sanghyun Hong 001; Stefan Lee (CoRR, 2024)

PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips. - Zachary Coalson; Jeonghyun Woo; Shiyang Chen 004; Yu Sun; Lishan Yang; Prashant J. Nair; Bo Fang 002; Sanghyun Hong 001 (CoRR, 2024)

SoK: Watermarking for AI-Generated Content. - Xuandong Zhao; Sam Gunn; Miranda Christ; Jaiden Fairoze; Andres Fabrega; Nicholas Carlini; Sanjam Garg; Sanghyun Hong 001; Milad Nasr; Florian Tramèr; Somesh Jha; Lei Li 005; et al. (SP, 2025)

Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations. - Sanghyun Hong 001; Fan Wu; Anthony Gruber; Kookjin Lee (CoRR, 2025)

Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions. - Tahmid Hasan Prato; Seijoon Kim; Lizhong Chen; Sanghyun Hong 001 (CoRR, 2025)

Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models. - Sungbok Shin; Hyeon Jeon; Sanghyun Hong 001; Niklas Elmqvist (CoRR, 2025)

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation. - Eunjin Roh; Yigitcan Kaya; Christopher Kruegel; Giovanni Vigna; Sanghyun Hong 001 (CoRR, 2025)

IF-GUIDE: Influence Function-Guided Detoxification of LLMs. - Zachary Coalson; Juhan Bae; Nicholas Carlini; Sanghyun Hong 001 (CoRR, 2025)

Harnessing Input-Adaptive Inference for Efficient VLN. - Dongwoo Kang; Akhil Perincherry; Zachary Coalson; Aiden Gabriel; Stefan Lee; Sanghyun Hong 001 (CoRR, 2025)