22 citations · 53 across the 6 of their papers we have counts for
6 papers
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning
Hong Joo Lee, Yong Man Ro
Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense…
Advancing Adversarial Training by Injecting Booster Signal
Hong Joo Lee, Youngjoon Yu, Yong Man Ro
Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have be…
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Youngjoon Yu, Hong Joo Lee, Hakmin Lee +1
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of pe…
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim +2
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive p…
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Hong Joo Lee, Seong Tae Kim, Hakmin Lee +2
Recent studies have shown that ensemble approaches could not only improve accuracy and but also estimate model uncertainty in deep learning. However, it requires a large number of…
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Hakmin Lee, Hong Joo Lee, Seong Tae Kim +1
Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not re…