most citedA Convolutional LSTM based Residual Network for Deepfake Video Detection

16 citations · 39 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV202116 cited

Am I a Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs under Deepfake Impersonation Attack

Shahroz Tariq, Sowon Jeon, Simon S. Woo

Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer hig…

cs.CV202016 cited

A Convolutional LSTM based Residual Network for Deepfake Video Detection

Shahroz Tariq, Sangyup Lee, Simon S. Woo

In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake…

cs.CV20206 cited

T-GD: Transferable GAN-generated Images Detection Framework

Hyeonseong Jeon, Youngoh Bang, Junyaup Kim +1

Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detectin…

cs.CR20201 cited

Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks

William Aiken, Hyoungshick Kim, Simon Woo

Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into embedding copyright protection for neural networks has be…

cs.CV2020

FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network

Hyeonseong Jeon, Youngoh Bang, Simon S. Woo

Creating fake images and videos such as "Deepfake" has become much easier these days due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent research suc…