106 citations · 139 across the 5 of their papers we have counts for
5 papers
Effective Targeted Attacks for Adversarial Self-Supervised Learning
Minseon Kim, Hyeonjeong Ha, Sooel Son +1
Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised A…
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
Suyoung Lee, Wonho Song, Suman Jana +2
Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners. In this paper, we argue that…
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…
Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned
Dongkwan Kim, Eunsoo Kim, Sang Kil Cha +2
Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability dis…
Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer
Suyoung Lee, HyungSeok Han, Sang Kil Cha +1
JavaScript (JS) engine vulnerabilities pose significant security threats affecting billions of web browsers. While fuzzing is a prevalent technique for finding such vulnerabilities…