Images are often tinkered with the intention to benefit one party. In reality, images are often considered as solid evidence to prove something concrete. Therefore, fake news or any form of information that has been manipulated in such a way it benefits the party in fault or results in misleading. One of the biggest sources of news or information manipulation is image falsification. To detect the image falsification, it takes a substantial amount of image data and robust model that can process every pixel but still provides efficiency and flexibility to support daily life use. In the era, where huge amount of data is generated every second, Deep learning is the right solution because deep learning thrives as the dataset increases in size. Therefore, Convolutional Neural Network (CNN) in combination with Error Level Analysis (ELA) to facilitate model computing, Detection of a forged image has achieved an accuracy of 91.33% and convergence with only 9 epochs.
Key words: Image, Forgery, Error, Deep Learning, Analysis, Fake, News, Machine Learning
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