Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks

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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“…2022: The emergence of deepfake technologies security [10], solving the creation of fake news articles and other written content.…”

Section: Historical Development Of Deepfakementioning

confidence: 99%

“…In recent times, a significant amount of research [9][10][11] has been performed in the area of deepfake. As a result, various review papers have compiled summaries in various disciplines.…”

Section: Introductionmentioning

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 increasing sophistication of deepfake generation algorithms, the ability to accurately detect these videos has become a critical challenge for researchers and developers. One approach to deepfake detection is through the use of Convolutional Neural Networks (CNNs) [1]. These networks have proven to be highly effective in image and video recognition tasks, making them a natural choice for detecting manipulated video content.…”

Section: Introductionmentioning

confidence: 99%

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

Hussain¹,

Ibraheem

2023

MJCS

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Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.

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“…The accuracy of existing techniques in detecting deepfake videos and images is average 90% [14,15]. However, this has further reduced to 70% with the introduction of facemasks during the Covid-19 pandemic [16][17][18]. This has motivated criminals to use facemasks to elude and cover their criminal activities by editing surveillance videos to escape the criminal justice system.…”

Section: Introductionmentioning

confidence: 99%

DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

et al. 2023

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Deepfake is a technology that creates fake images and videos with replaced or synthesized faces. Deepfakes are becoming a concerning social phenomenon, as they can be maliciously used to generate false political news, disseminate dangerous information, falsify electronic evidence, and commit digital harassment and fraud. The ease and accuracy of creating Deepfakes have been bolstered by the popularity of wearing face masks since the beginning of the infectious disease outbreak (2020). Because these masks obstruct defining facial features, fake videos are now even more challenging to identify, increasing the necessity for advanced Deepfake detection technology. The research also creates a real/fake video dataset with face masks because the field lacks the dataset required for detection-model training. The proposed research proposes a Deepfake Face Mask Dataset (DFFMD) based on a novel Inception-ResNet-v2 with preprocessing stages, featurebased, residual connection, and batch normalization. The combination of preprocessing stages, feature-based, residual connection, and batch normalization increases the detection accuracy of deepfake videos in the presence of facemasks, unlike the traditional methods. The study's results compared with existing state-of-the-art methods detect face-mask-Deepfakes with 99.81% accuracy compared to the traditional InceptionResNetV2 and VGG19, whose accuracy is 77.48%, and 99.25%, respectively. Future work should evaluate the accuracy of developing a subsequent experimental work for increased detection of deepfake with facemasks.

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Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks

Cited by 126 publications

References 52 publications

An Investigation of the Effectiveness of Deepfake Models and Tools

An Investigation of the Effectiveness of Deepfake Models and Tools

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

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