An integrated spatiotemporal-based methodology for deepfake detection

Ismail, Aya Abdelsalam; Elpeltagy, Marwa S.; Zaki, Mervat S.; ElDahshan, Kamal A.

doi:10.1007/s00521-022-07633-3

Cited by 15 publications

(4 citation statements)

References 64 publications

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“…The main drawback is the lack of important architecture information like the feature map's size resulting from both, ResNet50 and ConvGRU. (Ismail et al, 2022) proposed a hybrid approach for face forgey classification in videos that integrates image features extracted from a modified Xception Net architecture and spatial gradient directions computed from the Histogram of Gradient Oriented (HOG) method. Their strategy presented a customized CNN architecture that receives the image containing the gradient orientation calculated by the HOG method and produces a fixed-size output feature vector representation.…”

Section: Grumentioning

confidence: 99%

A review of deep learning‐based approaches for deepfake content detection

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning‐based approaches. We aim to broaden the state‐of‐the‐art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.

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Section: Grumentioning

confidence: 99%

A review of deep learning‐based approaches for deepfake content detection

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

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“…Some other detection methods are data-driven that do not target any specific artifacts and distinguish the manipulation by classification [3]. The deepfake visual video detection methods can be categorized into Convolution Neural Network (CNN)-based methods [19,20,21,22], methods that are based on CNN with a temporal network [23,24,25,26,27], handcrafted feature-based methods [28], and handcrafted feature-based methods with deep networks [29,30]. This is illustrated in Fig.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Then, the output of both was fed into the capsule network to individuate deepfakes. Ismail et al [30] introduced a hybrid method in which two feature extraction methods were employed to learn and extract enrich spatial features from the detected face frames of video. These methods were a CNN that was based on the Histogram of Oriented Gradient (HOG) method and the improved XceptionNet.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Smart Deepfake Video Detection System

Elpeltagy¹,

Ismail²,

Zaki³

et al. 2023

IJACSA

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Rapid advancements in deep learning-based technologies have developed several synthetic video and audio generation methods producing incredibly hyper-realistic deepfakes. These deepfakes can be employed to impersonate the identity of a source person in videos by swapping the source's face with the target one. Deepfakes can also be used to clone the voice of a target person utilizing audio samples. Such deepfakes may pose a threat to societies if they are utilized maliciously. Consequently, distinguishing either one or both deepfake visual video frames and cloned voices from genuine ones has become an urgent issue. This work presents a novel smart deepfake video detection system. The video frames and audio are extracted from given videos. Two feature extraction methods are proposed, one for each modality; visual video frames, and audio. The first method is an upgraded XceptionNet model, which is utilized for extracting spatial features from video frames. It produces feature representation for visual video frames. The second one is a modified InceptionResNetV2 model based on the Constant-Q Transform (CQT) method. It is employed to extract deep timefrequency features from the audio modality. It produces feature representation for the audio. The corresponding extracted features of both modalities are fused at a mid-layer to produce a bimodal information-based feature representation for the whole video. These three representation levels are independently fed into the Gated Recurrent Unit (GRU) based attention mechanism helping to learn and extract deep and important temporal information per level. Then, the system checks whether the forgery is only applied to video frames, audio, or both, and produces the final decision about video authenticity. The newly suggested method has been evaluated on the FakeAVCeleb multimodal videos dataset. The experimental results analysis assures the superiority of the new method over the current-stateof-the-art methods.

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“…Ismail et al [72] proposed a hybrid approach for face forgey classification in videos that integrates image features extracted from a modified Xception Net architecture and spatial gradient directions computed from the Histogram of Gradient Oriented (HOG) method. Their strategy presented a customized CNN architecture that receives the image containing the gradient orientation calculated by the HOG method and produces a fixed-size output feature vector representation.…”

Section: Grumentioning

confidence: 99%

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Passos,

Jodas,

Costa

et al. 2023

Preprint

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

show abstract

An integrated spatiotemporal-based methodology for deepfake detection

Cited by 15 publications

References 64 publications

A review of deep learning‐based approaches for deepfake content detection

A review of deep learning‐based approaches for deepfake content detection

A Novel Smart Deepfake Video Detection System

A Review of Deep Learning-based Approaches for Deepfake Content Detection

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