Deepfake detection: A systematic literature review
Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …
resulted in new techniques and various tools for manipulating multimedia. Though the …
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 …
multimedia content can now provide a very advanced level of realism. The boundary …
End-to-end reconstruction-classification learning for face forgery detection
Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …
characteristics, local textures, or frequency statistics for forgery detection. This causes …
Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection
Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …
manipulation technologies cause serious worries. Though recent works have reached …
Learning self-consistency for deepfake detection
We propose a new method to detect deepfake images using the cue of the source feature
inconsistency within the forged images. It is based on the hypothesis that images' distinct …
inconsistency within the forged images. It is based on the hypothesis that images' distinct …
Id-reveal: Identity-aware deepfake video detection
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are
mostly trained to detect a specific fake method. As a result, these approaches show poor …
mostly trained to detect a specific fake method. As a result, these approaches show poor …
MTD-Net: Learning to detect deepfakes images by multi-scale texture difference
With the rapid development of face manipulation technology, it is difficult for human eyes to
distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …
distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …
Deepfakes generation and detection: a short survey
Z Akhtar - Journal of Imaging, 2023 - mdpi.com
Advancements in deep learning techniques and the availability of free, large databases
have made it possible, even for non-technical people, to either manipulate or generate …
have made it possible, even for non-technical people, to either manipulate or generate …
Deepfake detection using spatiotemporal convolutional networks
O De Lima, S Franklin, S Basu, B Karwoski… - arXiv preprint arXiv …, 2020 - arxiv.org
Better generative models and larger datasets have led to more realistic fake videos that can
fool the human eye but produce temporal and spatial artifacts that deep learning …
fool the human eye but produce temporal and spatial artifacts that deep learning …
Improving generalization by commonality learning in face forgery detection
This paper proposes a commonality learning strategy for face video forgery detection to
improve the generalization. Considering various face forgery methods could leave certain …
improve the generalization. Considering various face forgery methods could leave certain …