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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

A Deep Learning Based Method to Discriminate Between Photorealistic Computer Generated Images and Photographic Images

verfasst von : Kunj Bihari Meena, Vipin Tyagi

Erschienen in: Advances in Computing and Data Sciences

Verlag: Springer Singapore

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Abstract

The rapid development of multimedia tools has changed the digital world drastically. Consequently, several new technologies like virtual reality, 3D gaming, and VFX (Visual Effects) have emerged from the concept of computer graphics. These technologies have created a revolution in the entertainment world. However, photorealistic computer generated images can also play damaging roles in several ways. This paper proposes a deep learning based technique to differentiate computer generated images from photographic images. The idea of transfer learning is applied in which the weights of pre-trained deep convolutional neural network DenseNet-201 are transferred to train the SVM to classify the computer generated images and photographic images. The experimental results performed on the DSTok dataset show that the proposed technique outperforms other existing techniques.

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Metadaten
Titel
A Deep Learning Based Method to Discriminate Between Photorealistic Computer Generated Images and Photographic Images
verfasst von
Kunj Bihari Meena
Vipin Tyagi
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Advances in Computing and Data Sciences

Print ISBN: 978-981-15-6633-2

Electronic ISBN: 978-981-15-6634-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-6634-9_20

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