no code implementations • 22 Apr 2024 • Hanzhe Li, Yuezun Li, Jiaran Zhou, Bin Li, Junyu Dong
Existing methods typically generate these faces by blending real or fake faces in color space.
no code implementations • 22 Apr 2024 • Yunfei Li, Yuezun Li, Xin Wang, Jiaran Zhou, Junyu Dong
In this paper, we propose a novel Texture-aware and Shape-guided Transformer to enhance detection performance.
no code implementations • 17 Apr 2024 • Ying Zhang, Yuezun Li, Bo Peng, Jiaran Zhou, Huiyu Zhou, Junyu Dong
The task of video inpainting detection is to expose the pixel-level inpainted regions within a video sequence.
no code implementations • 17 Dec 2023 • Qingxuan Lv, Yuezun Li, Junyu Dong, Sheng Chen, Hui Yu, Huiyu Zhou, Shu Zhang
Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation.
no code implementations • 3 Aug 2023 • Cong Zhang, Honggang Qi, Yuezun Li, Siwei Lyu
DeepFakes have raised serious societal concerns, leading to a great surge in detection-based forensics methods in recent years.
no code implementations • 2 Aug 2023 • Jiucui Lu, Jiaran Zhou, Junyu Dong, Bin Li, Siwei Lyu, Yuezun Li
The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively.
no code implementations • 27 Jul 2023 • Pu Sun, Honggang Qi, Yuezun Li, Siwei Lyu
In light of these two traces, our method can effectively expose DeepFakes by identifying them.
1 code implementation • 22 Apr 2022 • Xianglong, Yuezun Li, Haipeng Qu, Junyu Dong
However, the guidance map is fixed in existing methods, which can not consistently reflect the behavior of networks as the image is changed during iteration.
no code implementations • 3 Jun 2021 • Quanyu Liao, Yuezun Li, Xin Wang, Bin Kong, Bin Zhu, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
1 code implementation • 2 Jun 2021 • Bo Peng, Hongxing Fan, Wei Wang, Jing Dong, Yuezun Li, Siwei Lyu, Qi Li, Zhenan Sun, Han Chen, Baoying Chen, Yanjie Hu, Shenghai Luo, Junrui Huang, Yutong Yao, Boyuan Liu, Hefei Ling, Guosheng Zhang, Zhiliang Xu, Changtao Miao, Changlei Lu, Shan He, Xiaoyan Wu, Wanyi Zhuang
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
no code implementations • 2 Mar 2021 • Yuezun Li, Cong Zhang, Pu Sun, Honggang Qi, Siwei Lyu
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
no code implementations • 1 Feb 2021 • Pu Sun, Yuezun Li, Honggang Qi, Siwei Lyu
In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality.
1 code implementation • 31 Oct 2020 • Pu Sun, Yuezun Li, Honggang Qi, Siwei Lyu
Face synthesis is an important problem in computer vision with many applications.
1 code implementation • 24 Sep 2020 • Shu Hu, Yuezun Li, Siwei Lyu
We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes.
no code implementations • 19 Oct 2019 • Yuezun Li, Ao Luo, Siwei Lyu
In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture.
7 code implementations • CVPR 2020 • Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, Siwei Lyu
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
no code implementations • 21 Jun 2019 • Yuezun Li, Xin Yang, Baoyuan Wu, Siwei Lyu
Recent years have seen fast development in synthesizing realistic human faces using AI technologies.
no code implementations • 30 Mar 2019 • Xin Yang, Yuezun Li, Honggang Qi, Siwei Lyu
Generative adversary networks (GANs) have recently led to highly realistic image synthesis results.
no code implementations • 12 Feb 2019 • Yuezun Li, Siwei Lyu
In this work, we describe a new face de-identification method that can preserve essential facial attributes in the faces while concealing the identities.
1 code implementation • 1 Nov 2018 • Xin Yang, Yuezun Li, Siwei Lyu
In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes).
3 code implementations • 1 Nov 2018 • Yuezun Li, Siwei Lyu
Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images.
no code implementations • 16 Sep 2018 • Yuezun Li, Xiao Bian, Ming-Ching Chang, Siwei Lyu
In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object.
no code implementations • 16 Sep 2018 • Yuezun Li, Daniel Tian, Ming-Ching Chang, Xiao Bian, Siwei Lyu
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms.
3 code implementations • 7 Jun 2018 • Yuezun Li, Ming-Ching Chang, Siwei Lyu
The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos.