Published Paper Details:

The focus of this work relates to the identification and tampering detection of forensic scanners through deep neural production techniques. Tools built on convolution neural networks (CNNs) and the CASIA dataset are employed to determine which type of scanner was used to create an image and locate portions that were edited. Among others, processes involve transforming the photographs into error level analysis (ELA) images in order to accentuate the inconsistencies and training a CNN in a procedure where the CNN architecture is optimized. The system achieves promising levels of performance and is thus appropriate for distinguishing between images that are or are not altered. As demonstrated by the series of experiments, the model was able to withstand different circumstances and enabled accuracy of over 82% during validation. This work has a considerable contribution to automated media forensics since it solves the problem of scanner identification and digital manipulation detection in an effective and scalable manner.