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http://dx.doi.org/10.9717/kmms.2021.24.12.1624

CNN-Based Fake Image Identification with Improved Generalization  

Lee, Jeonghan (Dept. of Electronic Engineering, Pukyong National University)
Park, Hanhoon (Dept. of Electronic Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.12, 2021 , pp. 1624-1631 More about this Journal
Abstract
With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.
Keywords
Fake Image Identification; Generative Adversarial Networks; Image Forensics; CNN; Generalization;
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