125 citations · 134 across the 5 of their papers we have counts for
5 papers
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Jung-Woo Chang, Nojan Sheybani, Shehzeen Samarah Hussain +3
Video compression plays a significant role in IoT devices for the efficient transport of visual data while satisfying all underlying bandwidth constraints. Deep learning-based vide…
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression
Jung-Woo Chang, Mojan Javaheripi, Seira Hidano +1
Video compression plays a crucial role in video streaming and classification systems by maximizing the end-user quality of experience (QoE) at a given bandwidth budget. In this pap…
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks
Saehyun Ahn, Jung-Woo Chang, Suk-Ju Kang
Deformable convolutional networks have demonstrated outstanding performance in object recognition tasks with an effective feature extraction. Unlike standard convolution, the defor…
Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs
Jung-Woo Chang, Saehyun Ahn, Keon-Woo Kang +1
Generative adversarial networks (GANs) have shown excellent performance in image and speech applications. GANs create impressive data primarily through a new type of operator calle…
An Energy-Efficient FPGA-based Deconvolutional Neural Networks Accelerator for Single Image Super-Resolution
Jung-Woo Chang, Keon-Woo Kang, Suk-Ju Kang
Convolutional neural networks (CNNs) demonstrate excellent performance in various computer vision applications. In recent years, FPGA-based CNN accelerators have been proposed for…