Deepfake Video Detection through Optical Flow Based CNN

Amerini, Irene; Galteri, Leonardo; Caldelli, Roberto; Bimbo, Alberto Del

doi:10.1109/iccvw.2019.00152

Cited by 281 publications

(128 citation statements)

References 11 publications

(11 reference statements)

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“…Güera and Delp [ 149 ] proposed to use a CNN for frame feature extraction and an LSTM for temporal sequence analysis to detect Deepfake videos which contained inconsistent frames. Amerini et al [ 150 ] investigated the use of optical flow vectors to detect discrepancies in motion across several frames using the PWC-Net model [ 151 ]. Optical flow is a vector computed on two consecutive frames to extract apparent motion between the observer and the scene itself.…”

Section: Other Specific Forensic Problemsmentioning

confidence: 99%

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Camacho

Wang

2021

J. Imaging

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Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

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Section: Other Specific Forensic Problemsmentioning

confidence: 99%

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Camacho

Wang

2021

J. Imaging

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“…Dataset. FaceForensics++ (FF++) [6] is a recently released benchmark dataset and widely used for performance evaluation of face forgery detection [12,21,22]. The dataset consists of 1,000 original videos and their manipulated counterparts created by four typical manipulation methods: Deep-Fakes (DF), Face2Face (F2F), FaceSwap (FS), NeuralTexures (NT).…”

Section: Experimental Settingmentioning

confidence: 99%

DeepFake Videos Detection Using Self-Supervised Decoupling Network

Zhang

Xie

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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With wide applications of facial manipulation technology, fake images and videos are becoming a great public concern. Although existing methods for face forgery detection could achieve fairly good results on public database, most of them perform poorly when the fake images/videos are compressed as they are usually done in social networks. To tackle this issue, a self-supervised decoupling network (SSDN), that incorporates compression irrelevance, is proposed in this paper. The proposed model learns two separate feature representations for the suspect videos, i.e., authenticity and compression. A joint self-supervised strategy is then utilized for feature decoupling, in which, the similarity decoupling is carried out by similarity learning on authentic features, whereas for adversarial decoupling, the proposed SSDN model is trained in an adversarial manner for robust feature learning. Experimental results show that the SSDN outperforms the state-ofthe-art methods for deepfake detection against compression attacks on public datasets, e.g., FaceForensics++.

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“…Sabir 等 [100] 同样提出利用卷积神经网络和循环神经网络进行级联的伪造视频检测的方案. Amerini 等 [96] 提出可以利用光流法检测视频帧间的差异性, 并将该差异性输入到卷积神经网络中完成伪造视频的检测任务. [104] 提出借助数据增强的方案提高伪造检测算法的泛化能力, 在图像预处理和数据增强环节采取包括图像翻转、高斯模糊、图像压缩, 以及多种增强方案组合的形式对训练数据进行处理, 以得到具有较好泛化能力的伪造检测模型.…”

Section: 伪造视频中往往还存在视频帧之间的不一致性因此可以通过提取帧间差异用于伪造视频的检测unclassified

Deep visual identity forgery and detection

彭春蕾¹,

高新波²,

王楠楠³

et al. 2021

Sci. Sin.-Inf.

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With the wide application of deep learning techniques in video and image generation, the quality of visual identity forgery, especially face forgery, is becoming increasingly high. The detection of visual identity forgery has been a hot issue because of its important influence on both national security and social stability. In this paper, we introduce recent researches on deep visual identity forgery from target-specific face forgery and target-generic face forgery. We further summarize the techniques of detecting visual identity forgery from multiple categories, including the spatial clue based methods, the temporal clue based method, the techniques for generalizable forgery detection and spoofing forgery detection models. We later present the public datasets and performance of representative approaches on these datasets. Finally, the issues and challenges in existing research are discussed.

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Deepfake Video Detection through Optical Flow Based CNN

Cited by 281 publications

References 11 publications

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

DeepFake Videos Detection Using Self-Supervised Decoupling Network

Deep visual identity forgery and detection

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