22 citations · 64 across the 10 of their papers we have counts for
7 papers · 1 filter
VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild
Kun Cheng, Xiaodong Cun, Yong Zhang +6
We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even wi…
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Zeyu Qin, Yanbo Fan, Yi Liu +4
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work…
VDTR: Video Deblurring with Transformer
Mingdeng Cao, Yanbo Fan, Yong Zhang +2
Video deblurring is still an unsolved problem due to the challenging spatio-temporal modeling process. While existing convolutional neural network-based methods show a limited capa…
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Liang Chen, Yong Zhang, Yibing Song +2
Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging…
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
Fei Yin, Yong Zhang, Xiaodong Cun +7
One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. One challenging qua…
LAS-AT: Adversarial Training with Learnable Attack Strategy
Xiaojun Jia, Yong Zhang, Baoyuan Wu +3
Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples…