Media forensics and deepfakes: an overview

L Verdoliva - IEEE journal of selected topics in signal …, 2020 - ieeexplore.ieee.org
With the rapid progress in recent years, techniques that generate and manipulate
multimedia content can now provide a very advanced level of realism. The boundary …

Visual perception enabled industry intelligence: state of the art, challenges and prospects

J Yang, C Wang, B Jiang, H Song… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Visual perception refers to the process of organizing, identifying, and interpreting visual
information in environmental awareness and understanding. With the rapid progress of …

Towards universal fake image detectors that generalize across generative models

U Ojha, Y Li, YJ Lee - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

Implicit identity leakage: The stumbling block to improving deepfake detection generalization

S Dong, J Wang, R Ji, J Liang… - Proceedings of the …, 2023 - openaccess.thecvf.com
In this paper, we analyse the generalization ability of binary classifiers for the task of
deepfake detection. We find that the stumbling block to their generalization is caused by the …

Multi-attentional deepfake detection

H Zhao, W Zhou, D Chen, T Wei… - Proceedings of the …, 2021 - openaccess.thecvf.com
Face forgery by deepfake is widely spread over the internet and has raised severe societal
concerns. Recently, how to detect such forgery contents has become a hot research topic …

Spatial-phase shallow learning: rethinking face forgery detection in frequency domain

H Liu, X Li, W Zhou, Y Chen, Y He… - Proceedings of the …, 2021 - openaccess.thecvf.com
The remarkable success in face forgery techniques has received considerable attention in
computer vision due to security concerns. We observe that up-sampling is a necessary step …

Thinking in frequency: Face forgery detection by mining frequency-aware clues

Y Qian, G Yin, L Sheng, Z Chen, J Shao - European conference on …, 2020 - Springer
As realistic facial manipulation technologies have achieved remarkable progress, social
concerns about potential malicious abuse of these technologies bring out an emerging …

Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection

J Li, H **e, J Li, Z Wang… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …

M2tr: Multi-modal multi-scale transformers for deepfake detection

J Wang, Z Wu, W Ouyang, X Han, J Chen… - Proceedings of the …, 2022 - dl.acm.org
The widespread dissemination of Deepfakes demands effective approaches that can detect
perceptually convincing forged images. In this paper, we aim to capture the subtle …

Face x-ray for more general face forgery detection

L Li, J Bao, T Zhang, H Yang, D Chen… - Proceedings of the …, 2020 - openaccess.thecvf.com
In this paper we propose a novel image representation called face X-ray for detecting
forgery in face images. The face X-ray of an input face image is a greyscale image that …