Abstract
With the advent of fake news and propaganda being spread throughout the Internet using forged or computer-generated images, it is important to evolve algorithms that are able to differentiate between computer-generated images and natural ones. In this paper, we provide a high-level summary of the methods proposed recently which classify images as computer generated or natural using deep learning concepts. We spelled out the pros and cons of each method and further suggested future research paths like building a standard computer generated (CG) versus natural images (NI) dataset targeting compressed images from heterogeneous sources which ensures that the dataset models real world well, testing proposed approaches in real-life conditions and trying out training classifiers using combination of features generated from various convolutional neural networks (CNNs) as opposed to a single neural network to assess its impact on the accuracy rate for classification of compressed images.
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Meenai, A., Khan, V. (2020). Survey of Methods Applying Deep Learning to Distinguish Between Computer Generated and Natural Images. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, vol 100. Springer, Singapore. https://doi.org/10.1007/978-981-15-2071-6_18
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DOI: https://doi.org/10.1007/978-981-15-2071-6_18
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