Abstract
The recent and rapid growth in image manipulation techniques raises grave concerns with respect to document forgery. It also diminishes trust in the data available publicly and can cause harm by spreading false information. In this paper, the problem of facial forgery by training a neural network to distinguish between real and fake faces has been addressed. The convolutional neural network has been trained with the help of transfer learning, which allows us to utilize computer vision models like ResNet and AlexNet, which were pre-trained on massive datasets. The network was evaluated on a dataset of 2041 images provided by the computational Intelligence and Photography Lab at the Department of Computer Science, Yonsei University. Results show that ResNet-152 is able to detect facial forgery with an accuracy of up to 76.79%.
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Chandani, K., Arora, M. (2021). Automatic Facial Forgery Detection Using Deep Neural Networks. In: Kumar, N., Tibor, S., Sindhwani, R., Lee, J., Srivastava, P. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9956-9_21
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DOI: https://doi.org/10.1007/978-981-15-9956-9_21
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