Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics

Li, Yuezun; Yang, Xin; Sun, Peipei; Qi, Honggang; Lyu, Siwei

doi:10.1109/cvpr42600.2020.00327

Cited by 850 publications

(712 citation statements)

References 29 publications

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“…Video frames with visual artefacts. Deepfake generated image shows colour difference and resolution inconsistency because of the lack of postprocess Thus, this method is not effective for detecting a new version of deepfake videos as deepfake algorithms evolve [8].…”

Section: F I G U R Ementioning

confidence: 99%

“…To begin with, the number of video datasets built for deepfake detection tasks is growing. From small datasets (such as DeepFake-TIMIT [5] and UADFV [6]) in an early stage, to large-scale datasets (such as FaceForensic++ [7], Celeb-DF [8], DFDC [9] and DeeperForensic [10]), the number of datasets that can be used for training has increased. Furthermore, several research institutions are becoming aware of the dangers of deepfake videos and trying to promote related research.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Deepfake Video Detection

Xia

Fei

et al. 2021

IET Biometrics

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Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target at celebrities or politicians. These videos are often used to damage the reputation of celebrities and guide public opinion, greatly threatening social stability. Although the deepfake algorithm itself has no attributes of good or evil, this technology has been widely used for negative purposes. To prevent it from threatening human society, a series of research have been launched, including developing detection methods and building large-scale benchmarks. This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks. It has been revealed that current detection methods are still insufficient to be applied in real scenes, and further research should pay more attention to the generalization and robustness.This is an open access article under the terms of the Creative Commons Attribution-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited and no modifications or adaptations are made.

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Section: F I G U R Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Deepfake Video Detection

Xia

Fei

et al. 2021

IET Biometrics

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“…The encoding of the source is then concatenated to the difference of the encoding of the other two elements and input to a decoder for classification. The model was tested on FaceForensics++ (FF++) [34], Celeb-DF [35] and DFDC-P [36] datasets and achieved comparable results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In FakeSpotter [54], Wang et al managed to overcome noise and distortions by monitoring the pattern of each layer's neuron activation of a face recognition network to seize the fine features that could help in detecting fakes. They tested their model on FF++, DFDC [55] and Celeb-DF [35] and produced comparable results. In DeepfakeStack [56], the authors followed a Greedy Layer-wise Pretraining technique to train seven deep learning models (base-leaners) with ImageNet weights which were computationally very expensive.…”

Section: Literature Reviewmentioning

confidence: 99%

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iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection

Khalil¹,

Youssef²,

Saleh³

2021

Future Internet

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Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. Two feature extraction methods are combined, texture-based Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) based modified High-Resolution Network (HRNet), along with an application of capsule neural networks (CapsNets) implementing a concurrent routing technique. Experiments have been conducted on large benchmark datasets to evaluate the performance of the proposed model. Several performance metrics are applied and experimental results are analyzed. The proposed model was primarily trained and tested on the DeepFakeDetectionChallenge-Preview (DFDC-P) dataset then tested on Celeb-DF to examine its generalization capability. Experiments achieved an Area-Under Curve (AUC) score improvement of 20.25% over state-of-the-art models.

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A lightweight 3D convolutional neural network for deepfake detection

Liu

Zhu

et al. 2021

Int J of Intelligent Sys

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The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, among which three‐dimensional (3D) convolution neural networks (CNN) have attracted wide interest and achieved satisfying performances. However, there are few 3D CNNs designed for DeepFake detection and the parameters of them are large, which cause heavy memory and storage consumption. In this paper, a lightweight 3D CNN is proposed for DeepFake detection. Channel transformation module is designed to extract features with much fewer parameters in higher level. Serving as spatial‐temporal module, 3D CNNs are adopted to fuse the spatial features in time dimension. To suppress frame content and highlight frame texture, spatial rich model features are extracted from the input frames, which helps the spatial‐temporal module achieve better performance. Experimental results show that the number of parameters of the proposed network is much less than those of other networks and the proposed network outperforms other state‐of‐the‐art DeepFake detection methods on mainstream DeepFake data sets.

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Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics

Cited by 850 publications

References 29 publications

A Survey on Deepfake Video Detection

A Survey on Deepfake Video Detection

iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection

A lightweight 3D convolutional neural network for deepfake detection

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