R
Run Wang
Researcher at Wuhan University
Publications - 41
Citations - 705
Run Wang is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 11, co-authored 35 publications receiving 381 citations. Previous affiliations of Run Wang include Chinese Ministry of Education & Nanyang Technological University.
Papers
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Proceedings ArticleDOI
FakeSpotter: A simple yet robust baseline for spotting AI-synthesized fake faces
TL;DR: This work proposes a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces, conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector.
Posted Content
Towards a Robust Deep Neural Network in Texts: A Survey
TL;DR: A taxonomy of adversarial attacks and defenses in texts from the perspective of different natural language processing (NLP) tasks is given, and how to build a robust DNN model via testing and verification is introduced.
Proceedings ArticleDOI
DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices
TL;DR: This work proposes a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices, and poses a new insight into adopting neuron behaviors for effective and robust AI aided multimedia fakes forensics as an inside-out approach.
Posted Content
FakeLocator: Robust Localization of GAN-Based Face Manipulations
Yihao Huang,Felix Juefei-Xu,Run Wang,Qing Guo,Xiaofei Xie,Lei Ma,Jianwen Li,Weikai Miao,Yang Liu,Geguang Pu +9 more
TL;DR: The proposed FakeLocator can obtain high localization accuracy, at full resolution, on manipulated facial images, and is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.
Proceedings ArticleDOI
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
Yihao Huang,Felix Juefei-Xu,Run Wang,Qing Guo,Lei Ma,Xiaofei Xie,Jianwen Li,Weikai Miao,Yang Liu,Geguang Pu +9 more
TL;DR: Through reducing artifact patterns, the FakePolisher technique significantly reduces the accuracy of the 3 state-of-the-art fake image detection methods, i.e., 47% on average and up to 93% in the worst case.