Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

Nguyen, Xuan Hau; Tran, Thai Son; Le, Van Thinh; Nguyen, Kim Duy; Truong, Dinh-Tu

doi:10.1016/j.fsidi.2021.301108

Cited by 26 publications

(11 citation statements)

References 4 publications

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“…The point when information is gathered over both space also time. Nguyen et al [37] could take spatio-temporal characteristics from a contiguous frame sequence and learn them using a 3-D CNN network model to achieve over 99% as deepfake detection accuracy. Dynamic prototype network (DPNet) a powerful result that uses dynamic representations.…”

Section: Deepfake Detection 31 Deep Learning Based Methodsmentioning

confidence: 99%

A survey of deepfakes in terms of deep learning and multimedia forensics

Hadi

Kadhem²,

Abbas³

2022

IJECE

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.

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Section: Deepfake Detection 31 Deep Learning Based Methodsmentioning

confidence: 99%

A survey of deepfakes in terms of deep learning and multimedia forensics

Hadi

Kadhem²,

Abbas³

2022

IJECE

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“…Chen et al [41] proposed a unified framework that takes into account the spatial features within a single frame and the temporal inconsistencies between frames. Similarly, Nguyen et al [42] proposed a three-dimensional CNN model that can learn spatio-temporal features from an adjacent frame sequence in the video.…”

Section: Cnn-based Methodsmentioning

confidence: 99%

“…Similarly, Nguyen et al. [42] proposed a three‐dimensional CNN model that can learn spatio‐temporal features from an adjacent frame sequence in the video.…”

Section: Related Workmentioning

confidence: 99%

Deep forgery discriminator via image degradation analysis

Zhang

et al. 2021

IET Image Processing

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Generative adversarial network-based deep generative model is widely applied in creating hyper-realistic face-swapping images and videos. However, its malicious use has posed a great threat to online contents, thus making detecting the authenticity of images and videos a tricky task. Most of the existing detection methods are only suitable for one type of forgery and only work for low-quality tampered images, restricting their applications. This paper concerns the construction of a novel discriminator with better comprehensive capabilities. Through analysis of the visual characteristics of manipulated images from the perspective of image quality, it is revealed that the synthesized face does have different degrees of quality degradation compared to the source content. Therefore, several kinds of image quality-related handicraft features are extracted, including texture, sharpness, frequency domain features, and deep features, to unveil the inconsistent information and modification traces in the fake faces. In this way, a 1065-dimensional vector of each image is obtained through multi-feature fusion, and it is then fed into RF to train a targeted binary classification detector. Extensive experiments have shown that the proposed scheme is superior to the previous methods in recognition accuracy on multiple manipulation databases including the Celeb-DF database with better visual quality.

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“…These spatio-temporal models can transfer some of their understanding of already learned deepfake methods onto new unseen fake kinds. Xuan HauNguyen et al [2] used 3D convolution kernels to con-struct deep 3D CNN models that extracts spatio-temporal features from frames sequence for detecting deepfakes. David Güera et al [3] have used RNN in their approach to detect deepfakes.…”

Section: Related Workmentioning

confidence: 99%

AFMB-Net

Vinay

Khurana

Sudarshan

et al. 2022

Teh. glas. (Online)

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With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes.

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Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

Cited by 26 publications

References 4 publications

A survey of deepfakes in terms of deep learning and multimedia forensics

A survey of deepfakes in terms of deep learning and multimedia forensics

Deep forgery discriminator via image degradation analysis

AFMB-Net

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