Edinburgh Napier University

  The advancement of technology has undoubtedly exposed everyone to a remarkable array of visual imagery. Nowadays, digital technology is eating away the trust and historical confidence people have in the integrity of imagery. Deep learning is often used for the detection of forged digital images through the classification of images as original or forged. Despite many advantages of deep learning algorithms to predict fake images such as automatic feature engineering, parameter sharing and dimensionality reduction, one of the drawbacks of deep learning emanates from parsing bad examples to deep learning models. In this work, cryptography was applied to improve the integrity of images used for deep learning (Convolutional Neural Network-CNN) based prediction using SHA-256. Our results after a hashing algorithm was used at a threshold of 0.0003 gives 73.20% image prediction accuracy. The use of CNN algorithm on the hashing image dataset gives a prediction accuracy of 72.70% at 0.09s. Furthermore, the result of CNN on the raw image dataset gives a prediction accuracy of 89.08% at 2s. The result shows that although a higher prediction accuracy is obtained when the CNN algorithm is used on the raw image without hashing, the prediction using the CNN algorithm with hashing is faster.