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Open AccessJournal ArticleDOI

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

TLDR
This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, with special attention to the latest generation of DeepFakes.
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This article is published in Information Fusion.The article was published on 2020-01-01 and is currently open access. It has received 502 citations till now.

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Citations
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Proceedings ArticleDOI

A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild

TL;DR: This work investigates the problem of lip-syncing a talking face video of an arbitrary identity to match a target speech segment, and identifies key reasons pertaining to this and hence resolves them by learning from a powerful lip-sync discriminator.
Posted Content

Two-branch Recurrent Network for Isolating Deepfakes in Videos

TL;DR: A method for deepfake detection based on a two-branch network structure that isolates digitally manipulated faces by learning to amplify artifacts while suppressing the high-level face content, and derives a novel cost function that compresses the variability of natural faces and pushes away the unrealistic facial samples in the feature space.
Posted ContentDOI

Deep Learning for Deepfakes Creation and Detection: A Survey

TL;DR: This study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
Proceedings ArticleDOI

Detecting Deep-Fake Videos From Phoneme-Viseme Mismatches

TL;DR: This work describes a technique to detect manipulated videos by exploiting the fact that the dynamics of the mouth shape – visemes – are occasionally inconsistent with a spoken phoneme, and demonstrates the efficacy and robustness of this approach to detect different types of deep-fake videos, including in thewild deep fakes.
Proceedings ArticleDOI

A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild

TL;DR: Wav2Lip as mentioned in this paper proposes a powerful lip-sync discriminator to resolve the problem of significant parts of the video being out-of-sync with the new audio.
References
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Proceedings ArticleDOI

Deep Residual Learning for Image Recognition

TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI

ImageNet: A large-scale hierarchical image database

TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI

Going deeper with convolutions

TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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