148 citations · 215 across the 14 of their papers we have counts for
15 papers
Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks
Jaehui Hwang, Huan Zhang, Jun-Ho Choi +2
Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understandin…
Amicable Aid: Perturbing Images to Improve Classification Performance
Juyeop Kim, Jun-Ho Choi, Soobeom Jang +1
While adversarial perturbation of images to attack deep image classification models pose serious security concerns in practice, this paper suggests a novel paradigm where the conce…
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…
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks
Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim +2
Recently, the vulnerability of deep image classification models to adversarial attacks has been investigated. However, such an issue has not been thoroughly studied for image-to-im…
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…