홍상현 오레곤 주립 대학교

Oregon State University
Computer Science Dept.
Cybersecurity | AI
Contact Information

Office: Room 4103, Kelley Engineering Center (KEC)
2500 NW Monroe Ave
Corvallis, OR 97331 USA
Office Hours: Tu/Th: 2 - 3 pm

          

Press

04.2022

TechXplore
Techradar.pro

06.2021

TechTalks

05.2021

Dev Podcast
MIT Tech Review

02.2021

USENIX Enigma 2021
(Ted Talk for Security)

Teaching

Spring 23 CS370: Intro to Sec.
CS499/599: MLSec.
Winter 23 CS344: OS I
Spring 22 CS344: OS I
Winter 21 CS499/599: MLSec.
Students [Full list]

Gabriel Ritter (PhD, CS,
  co-advise w. Rakesh Bobba)
Hoang Le (MS, CS)
Eunjin Roh (MS, CS)
Zach Coalson (BS, CS)

Alumni

2022: Ryan Little (BS, CS)
  Now a PhD student at UMD

Referred Papers


Color Palettes: Conferences | Journals | Workshops | Preprints
2022

Handcrafted Backdoors in Deep Neural Networks
Sanghyun Hong, Nicholas Carlini, and Alexey Kurakin
Advances in Neural Information Processing Systems (NeurIPS) 2022.
To Appear

A Scanner Deeply: Predicting Gaze Heatmaps on Visualizations Using Crowdsourced Eye Movement Data
Sungbok Shin, Sunghyo Chung, Sanghyun Hong, Niklas Elmqvist
IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2022), 2022. (IF: 5.226)
To Appear

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong,
Nicholas Carlini
(*authors ordered reverse-alphabetically)
The ACM Conference on Computer and Communications Security (CCS), 2022.
PDF | Media

AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
Seunghyeon Cho, Sanghyun Hong, Kookjin Lee, Noseong Park
International Conference on Machine Learning (ICML) Workshop
on Continuous-Time Methods for Machine Learning 2022.
PDF | Code

Improving Cross-Platform Binary Analysis Using Representation Learning via Graph Alignment
Geunwoo Kim, Sanghyun Hong, Michael Franz, Dokyung Song
The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2022.
PDF | Code

Data Poisoning Won't Save You From Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramer
International Conference on Learning Representations (ICLR) 2022.
PDF | Code | Poster

2021

Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, and Tudor Dumitraș
Advances in Neural Information Processing Systems (NeurIPS) 2021.
PDF | Code | Poster

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
*Sanghyun Hong, *Yigitcan Kaya, Ionuţ-Vlad Modoranu, and Tudor Dumitraș(* equal contribution)
International Conference on Learning Representations (ICLR) 2021. [Spotlight]
PDF | Code | Spotlight Presentation

Certified Malware in South Korea: A Localized Study of Breaches of Trust
in Code-Signing PKI Ecosystem

Bumjun Kwon, Sanghyun Hong, Yuseok Jeon, Doowon Kim
International Conference on Information and Communications Security (ICICS) 2021.
PDF

A Sound Mind in a Vulnerable Body:
Practical Hardware Attacks on Deep Learning

Sanghyun Hong
USENIX Enigma (Enigma) 2021.
Presentation

2020

On the Effectiveness of Regularization Against Membership Inference Attacks
Yiǧitcan Kaya, Sanghyun Hong, and Tudor Dumitraş
arXiv Preprint 2020.
PDF

On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Sanghyun Hong, Varun Chandrasekaran, Yiǧitcan Kaya, Tudor Dumitraş, and Nicolas Papernot
arXiv Preprint 2020.
PDF | Code

How to 0wn NAS in Your Spare Time
Sanghyun Hong, Michael Davinroy, Yigitcan Kaya, Dana Dachman-Soled, and Tudor Dumitraș
International Conference on Learning Representations (ICLR) 2020.
PDF | Code | Poster

2019

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks
Under Hardware Fault Attacks

Sanghyun Hong, Pietro Frigo, Yigitcan Kaya, Cristiano Giuffrida, and Tudor Dumitraș
Proceedings of The 28th USENIX Security Symposium (USENIX Security) 2019.
PDF | Presentation

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitraș
International Conference on Machine Learning (ICML) 2019.
PDF | Code

Poster: On the Feasibility of Training Neural Networks with Visibly Watermarked Dataset
Sanghyun Hong, Tae-hoon Kim, Tudor Dumitraş, and Jonghyun Choi
The Network and Distributed System Security Symposium (NDSS) 2019.
PDF | Code | Poster

Peek-a-Boo: Inferring Program Behaviors in a Virtualized Infrastructure without Introspection
Sanghyun Hong, Alina Nicolae, Abhinav Srivastava, and Tudor Dumitraş
Computer & Security (COSE) 2019.
PDF

2018

Go Serverless: Securing Cloud via Serverless Design Patterns
Sanghyun Hong, Abhinav Srivastava, William Shambrook, and Tudor Dumitraș
10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) 2018.
PDF | Slides

PAGE: Answering Graph Pattern Queries via Knowledge Graph Embedding
Sanghyun Hong, Noseong Park, Tanmoy Chakraborty, Hyunjoong Kang, and Soonhyun Kwon
International Conference on Big Data (Big Data), 2018
Paper | Slides

On Integrating Knowledge Graph Embedding into SPARQL Query Processing
Soonhyun Kwon, Hyunjoong Kang, Sanghyun Hong, Kookjin Lee, and Noseong Park
IEEE International Conference on Web Services (ICWS), 2018
Paper

2017

SENA: Preserving Social Structure for Network Embedding
*Sanghyun Hong, *Tanmoy Chakraborty, Sungjin Ahn, Ghaith Husari, and Noseong Park
(* equal contribution)
ACM Conference on Hypertext and Social Media (ACM HT), 2017.
Paper

Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Rock Stevens, Octavian Suciu, Andrew Ruef, Sanghyun Hong, Michael Hicks, and Tudor Dumitraş
NeurIPS Workshop on Reliable Machine Learning in the Wild (NeurIPS), 2017.
Paper | Slides | Media