17 citations · 86 across the 28 of their papers we have counts for
14 papers · 1 filter
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
Tiewen Chen, Shanmin Yang, Shu Hu +4
Recent studies on deepfake detection have achieved promising results when training and testing faces are from the same dataset. However, their results severely degrade when confron…
Efficient Image Super-Resolution via Symmetric Visual Attention Network
Chengxu Wu, Qinrui Fan, Shu Hu +3
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms. Recently, efficient Super-Resolution (SR)…
CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition
Shanmin Yang, Hui Guo, Shu Hu +5
Deepfake technology poses a significant threat to security and social trust. Although existing detection methods have shown high performance in identifying forgeries within dataset…
Controlling Neural Style Transfer with Deep Reinforcement Learning
Chengming Feng, Jing Hu, Xin Wang +5
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters. In this paper, we propose t…
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Jing Hu, Ziwei Luo, Chengming Feng +7
Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challen…
Improving Fairness in Deepfake Detection
Yan Ju, Shu Hu, Shan Jia +2
Despite the development of effective deepfake detectors in recent years, recent studies have demonstrated that biases in the data used to train these detectors can lead to disparit…
Attacking Important Pixels for Anchor-free Detectors
Yunxu Xie, Shu Hu, Xin Wang +4
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change the prediction result. Existing adversarial attacks o…
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces
Hui Guo, Shu Hu, Xin Wang +2
AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social i…
PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems
Shu Hu, Chun-Hao Liu, Jayanta Dutta +3
Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled obje…
GAN-generated Faces Detection: A Survey and New Perspectives
Xin Wang, Hui Guo, Shu Hu +2
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters…
Robust Attentive Deep Neural Network for Exposing GAN-generated Faces
Hui Guo, Shu Hu, Xin Wang +2
GAN-based techniques that generate and synthesize realistic faces have caused severe social concerns and security problems. Existing methods for detecting GAN-generated faces can p…
Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces
Hui Guo, Shu Hu, Xin Wang +2
Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from…
TML-AP: Adversarial Attacks to Top- Multi-Label Learning
Shu Hu, Lipeng Ke, Xin Wang +1
Top- multi-label learning, which returns the top- predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search…
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
Shu Hu, Yuezun Li, Siwei Lyu
Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work,…