Search Results for author: Jun-Ho Choi

Found 15 papers, 8 papers with code

Temporal Shuffling for Defending Deep Action Recognition Models against Adversarial Attacks

no code implementations15 Dec 2021 Jaehui Hwang, huan zhang, Jun-Ho Choi, Cho-Jui Hsieh, Jong-Seok Lee

Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction.

Action Recognition

Amicable Aid: Perturbing Images to Improve Classification Performance

no code implementations9 Dec 2021 Juyeop Kim, Jun-Ho Choi, Soobeom Jang, Jong-Seok Lee

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 concept of image perturbation can benefit classification performance, which we call amicable aid.

Adversarial Attack Classification +2

Light Lies: Optical Adversarial Attack

no code implementations18 Jun 2021 Kyulim Kim, JeongSoo Kim, Seungri Song, Jun-Ho Choi, Chulmin Joo, Jong-Seok Lee

We present experiments based on both simulation and a real hardware optical system, from which the feasibility of the proposed optical attack is demonstrated.

Adversarial Attack Classification +2

Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack

no code implementations ICCV 2021 Jaehui Hwang, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee

In this paper, we study the structural vulnerability of deep learning-based action recognition models against the adversarial attack using the one frame attack that adds an inconspicuous perturbation to only a single frame of a given video clip.

Action Recognition Adversarial Attack

SRZoo: An integrated repository for super-resolution using deep learning

1 code implementation2 Jun 2020 Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee

In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place.

Image and Video Processing Multimedia

EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition

no code implementations29 Apr 2020 Jun-Ho Choi, Jong-Seok Lee

Human activity recognition using multiple sensors is a challenging but promising task in recent decades.

Human Activity Recognition

Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks

1 code implementation ICCV 2019 Jun-Ho Choi, huan zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee

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.

Image Super-Resolution

Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network

2 code implementations30 Nov 2018 Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers.

Image Super-Resolution

MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution

3 code implementations29 Nov 2018 Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon, Jong-Seok Lee

Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths.

Image Super-Resolution

Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

1 code implementation13 Sep 2018 Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively.

Image Super-Resolution

Generative adversarial network-based image super-resolution using perceptual content losses

1 code implementation13 Sep 2018 Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee

In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion.

Image Super-Resolution

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