Improving the Classification of Tiny Images for Forensic Analysis

Abstract

Forensics can be defined as the approach that connects with and uses in governments and different organizations in order to detect any malicious activity. Digital forensics has become an essential approach to cyber investigation. Image forensics is one of the most beneficial ways that are used in digital forensics in order to help investigators in cybercrimes. Therefore, investigators can discover some new evidence besides what is already available on their systems when they use some digital forensics techniques. This thesis focuses on identifying an image based on its contents, especially tiny images. We investigated ways to improve the performance of some data classification techniques, such as principal component analysis (PCA), K- nearest neighbors (KNN), and convolutional neural network (CNN). In order to test these different classification techniques, we used feature extraction in order to extract the most useful features that are used as inputs to the classifiers. Therefore, we used the CIFAR-10 dataset that contains many tiny images, which is 60,000 32 x 32 color images. Three different classification techniques are tested in order to identify the most accurate algorithm for classifying the tiny image of the CIFAR-10 dataset. The results of our experiments showed that the best results were achieved when we used the convolutional neural network (CNN). Therefore, CNN is the best classification algorithm to use since it produced the best results matching approximately 74.10% among the other two classification techniques that are used in this research, which are PCA and KNN.