148 citations · 189 across the 6 of their papers we have counts for
7 papers · 1 filter
Light Lies: Optical Adversarial Attack
Kyulim Kim, JeongSoo Kim, Seungri Song +3
A significant amount of work has been done on adversarial attacks that inject imperceptible noise to images to deteriorate the image classification performance of deep models. Howe…
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack
Jaehui Hwang, Jun-Hyuk Kim, Jun-Ho Choi +1
The video-based action recognition task has been extensively studied in recent years. In this paper, we study the structural vulnerability of deep learning-based action recognition…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim +2
Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications. Thi…
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon +1
In recent years, single image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) have made significant progress. However, due to the non-adaptive natu…
Generative adversarial network-based image super-resolution using perceptual content losses
Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi +1
In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a rec…