DFCatcher: A Deep CNN Model to Identify Deepfake Face Images

Dhar, Arpita; Biswas, Likhan; Achariec, Prima; Ahmed, Shemonti; Sultana, Abida; Karim, Dewan Ziaul; Parvez, Mohammad Zavid

doi:10.1109/tencon54134.2021.9707314

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…To create ensemble-like multi-attention networks for detecting deep fake media, this work attempts to provide a complete examination of the mentioned methods, structures, and mechanisms. The research in [18] attempts to address the difficulty of differentiating between real and fake pictures by developing an algorithm that can distinguish between real and fake pictures. The algorithm used in [18] seeks to differentiate between real images and deep fakes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The research in [18] attempts to address the difficulty of differentiating between real and fake pictures by developing an algorithm that can distinguish between real and fake pictures. The algorithm used in [18] seeks to differentiate between real images and deep fakes. The dataset was tested against five transfer learning methods as well as an 18-layered bespoke CNN model that was described in the research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dhar [18] 140K real and fake faces VGG16, DenseNet121 InceptionV3, VGG19, ResNet50 Shad et al [19] 140K real and fake faces DenseNet201, DenseNet169, ResNet50, VGG16, VGG19, VGGFace Masood et al [20] DeepFake…”

Section: B Pretrained Mobilenetmentioning

confidence: 99%

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Using Deep Learning to Recognize Fake Faces

Atwan,

Wedyan,

Albashish

et al. 2024

IJACSA

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In recent times, many fake faces have been created using deep learning and machine learning. Most fake faces made with deep learning are referred to as "deepfake photos." Our study's primary goal is to propose a useful framework for recognizing deep-fake photos using deep learning and transformative learning techniques. This paper proposed convolutional neural network (CNN) models based on deep transfer learning methodologies in which the designed classifier using global average pooling (GAP), dropout, and a dense layer with two neurons that use SoftMax are substituted for the final fully connected layer in the pretrained models. DenseNet201, the suggested framework, produced the best accuracy of 86.85% for both the deepfake and real picture datasets, while MobileNet produced a lower accuracy of 82.78%. The obtained experimental results showed that the proposed method outperformed other stateof-the-art fake picture discriminators in terms of performance. The proposed architecture helps cybersecurity specialists fight deepfake-related cybercrimes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%