High-Performance Image Splicing Detection utilizing Image Augmentation and Deep Learning

Nowadays, the hassle-free availability of innumerable, easy-to-use image editing software makes image manipulation widespread, and these forged images can be used for various malicious activities. One of the most common methods of digital image deception is image splicing, where multiple portions from different images are combined to make the spliced image. Image splicing can be detected based on machine learning or deep learning techniques. Deep learning-based methods usually provide better performance but with extremely large training data and training time needed, which increases the cost of the model because of its structural complexity. Hence, in this research, we present a Deep Convolution Neural network-based model where a pre-trained network, ResNet50, replaces the initial convolution layers, followed by adding and modifying multiple layers. We have implemented image augmentation to make the dataset diverse for appropriate training of our model. Results from experiments show that our simplified approach, which does not require large training data and time, can accurately differentiate between the spliced images and the authentic images with the best accuracy of 1 and average accuracy of 0.99 for the DSO-1 dataset and best accuracy of 0.96 and average accuracy of 0.95 for the Columbia dataset.