116 citations · 121 across the 6 of their papers we have counts for
6 papers
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Junyoung Byun, Myung-Joon Kwon, Seungju Cho +2
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial example…
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Junyoung Byun, Seungju Cho, Myung-Joon Kwon +2
The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical ap…
Geometrically Adaptive Dictionary Attack on Face Recognition
Junyoung Byun, Hyojun Go, Changick Kim
CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries…
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks
Junyoung Byun, Hyojun Go, Changick Kim
While deep neural networks show unprecedented performance in various tasks, the vulnerability to adversarial examples hinders their deployment in safety-critical systems. Many stud…
Robust Federated Learning with Noisy Labels
Seunghan Yang, Hyoungseob Park, Junyoung Byun +1
Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local dat…
BitNet: Learning-Based Bit-Depth Expansion
Junyoung Byun, Kyujin Shim, Changick Kim
Bit-depth is the number of bits for each color channel of a pixel in an image. Although many modern displays support unprecedented higher bit-depth to show more realistic and natur…