78 citations · 133 across the 7 of their papers we have counts for
7 papers
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future
Byunggill Joe, Insik Shin, Jihun Hamm
Recurrent models are frequently being used in online tasks such as autonomous driving, and a comprehensive study of their vulnerability is called for. Existing research is limited…
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks
Byunggill Joe, Akshay Mehra, Insik Shin +1
Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vita…
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection
Byunggill Joe, Jihun Hamm, Sung Ju Hwang +2
Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inpu…
Optimal Virtual Cluster-based Multiprocessor Scheduling
Arvind Easwaran, Insik Shin, Insup Lee
Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that fin…
Resource Efficient Isolation Mechanisms in Mixed-Criticality Scheduling
Xiaozhe Gu, Arvind Easwaran, Kieu-My Phan +1
Mixed-criticality real-time scheduling has been developed to improve resource utilization while guaranteeing safe execution of critical applications. These studies use optimistic r…
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
Byunggill Joe, Sung Ju Hwang, Insik Shin
Although deep neural networks have shown promising performances on various tasks, even achieving human-level performance on some, they are shown to be susceptible to incorrect pred…
SynFuzz: Efficient Concolic Execution via Branch Condition Synthesis
Wookhyun Han, Md Lutfor Rahman, Yuxuan Chen +3
Concolic execution is a powerful program analysis technique for exploring execution paths in a systematic manner. Compare to random-mutation-based fuzzing, concolic execution is es…