no code implementations • 1 Nov 2023 • Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni, Seira Hidano, Xinyu Zhang, Farinaz Koushanfar
Although there have been a number of adversarial attacks on ML-based wireless systems, the existing methods do not provide a comprehensive view including multi-modality of the source data, common physical layer components, and wireless domain constraints.
no code implementations • 4 Apr 2023 • Jung-Woo Chang, Nojan Sheybani, Shehzeen Samarah Hussain, Mojan Javaheripi, Seira Hidano, Farinaz Koushanfar
Experimental results demonstrate that NetFlick can successfully deteriorate the performance of video compression frameworks in both digital- and physical-settings and can be further extended to attack downstream video classification networks.
no code implementations • 18 Mar 2022 • Jung-Woo Chang, Mojan Javaheripi, Seira Hidano, Farinaz Koushanfar
In this paper, we conduct the first systematic study for adversarial attacks on deep learning-based video compression and downstream classification systems.
no code implementations • 9 Jun 2020 • Saehyun Ahn, Jung-Woo Chang, Suk-Ju Kang
In this paper, we present a novel approach to accelerate deformable convolution on FPGA.
no code implementations • 15 Nov 2019 • Jung-Woo Chang, Saehyun Ahn, Keon-Woo Kang, Suk-Ju Kang
To implement the DeConv layer in hardware, the state-of-the-art accelerator reduces the high computational complexity via the DeConv-to-Conv conversion and achieves the same results.