DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Khormali, Aminollah; Yuan, J.S.

doi:10.3390/app12062953

Cited by 33 publications

(19 citation statements)

References 96 publications

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“…Test metrics for our best validated model on the Celeb-DFv2 test set, compared to other works. The AUC-ROC metric of prior works were aggregated and compiled in the recent work of 34 .…”

Section: Modelmentioning

confidence: 99%

Enhancing Practicality and Efficiency of Deepfake Detection.

Balafrej,

Dahmane

2024

Preprint

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The proliferation of deepfake generation has become increasingly widespread. Current solutions for automatically detecting and classifying generated content require substantial computational resources, making them impractical for use by the average non-expert individual particularly from edge computing applications. In this paper, we propose a series of techniques to accelerate the inference speed of deepfake detection on video data. We also draw inspiration from steganalysis approaches to expose deepfakes as any secret payloads encoded in the image. Furthermore, some key considerations were identified to significantly reduce the size of the core convolutional neural network. The experiment yielded competitive results when evaluated on two second-generation deepfake datasets, namely Celeb-DFv2 and DFDC, while requiring only a fraction of the typical computational cost and resources.

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“…Test metrics for our best validated model on the Celeb-DFv2 test set, compared to other works. The AUC-ROC metric of prior works were aggregated and compiled in the recent work of 34 .…”

Section: Modelmentioning

confidence: 99%

Enhancing Practicality and Efficiency of Deepfake Detection.

Balafrej,

Dahmane

2024

Preprint

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“…This progress has encouraged researchers to develop novel deep neural network models for image forensics. Recent works based on CNNs [17][18][19][20], recurrent neural networks [4,21], and transformers [22][23][24] perform much better for Forgery detection than traditional methods.…”

Section: Rel Ated Workmentioning

confidence: 99%

ForensicNet: Modern convolutional neural network‐based image forgery detection network

Yadav

2023

Journal of Forensic Sciences

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The advancements in Image editing techniques produce realistic‐looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth‐wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth‐wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state‐of‐the‐art methods by a large margin.

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“…Transformers are highly well-known deep learning models, and they are now used in the field of deepfake generation and detection. The following transformer models can be used to generate and recognize deepfakes: EfficentNet+ViT [57], M2TR [63], CViT [65], ViT [100], ViT+Distillation [64], Video Transformer [101].…”

Section: Transformer-based Modelsmentioning

confidence: 99%

An Investigation of the Effectiveness of Deepfake Models and Tools

Mukta

Ahmad

Raiaan

et al. 2023

JSAN

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With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.

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DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Cited by 33 publications

References 96 publications

Enhancing Practicality and Efficiency of Deepfake Detection.

Enhancing Practicality and Efficiency of Deepfake Detection.

ForensicNet: Modern convolutional neural network‐based image forgery detection network

An Investigation of the Effectiveness of Deepfake Models and Tools

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