Dataset for classification of computer graphic images and photographic images

Abstract

The recent advancements in computer graphics (CG) image rendering techniques have made it easy for the content creators to produce high quality computer graphics similar to photographic images (PG) confounding the most naïve users. Such images used with negative intent, cause serious problems to the society. In such cases, proving the authenticity of an image is a big challenge in digital image forensics due to high photo-realism of CG images. Existing datasets used to assess the performance of classification models are lacking with: (i) larger dataset size, (ii) diversified image contents, and (iii) images generated with the recent digital image rendering techniques. To fill this gap, we created two new datasets, namely, ‘JSSSTU CG and PG image dataset’ and ‘JSSSTU PRCG image dataset’. Further, the complexity of the new datasets and benchmark datasets are evaluated using handcrafted texture feature descriptors such as gray level co-occurrence matrix, local binary pattern and VGG variants (VGG16 and VGG19) which are pre-trained convolutional neural network (CNN) models. Experimental results showed that the CNN-based pre-trained techniques outperformed the conventional support vector machine (SVM)-based classifier in terms of classification accuracy. Proposed datasets have attained a low f-score when compared to existing datasets indicating they are very challenging.