Modified CNN model-based Forgery Detection applied to Multiple-Resolution Tampered Images

Abstract

The crucial problem of forensic techniquesis is how to detect/recognize tampered images through public media platforms under the attactks of subjective modifications. Because of many accessible photoshop programs, an image/video such as in Facebook, Instagram, Reddit Twitter, etc. can be easily tampered to falsify the information within the image. Accoding to the requirement of an efficient method for detecting fake images, we have developed modifed CNN models which are combined with the super-resolution approach to solve this issue. In the paper, we present an appropriate method using CNN models to detect tampered images with the increase in resolutions of the tampered areas, the proposed model can detect and point out the areas that have been tampered. The ResNet50 and mUNet modified models are used for classification and segmentation respectively. With the developed models, the results were given with an accuracy of at least 90% on the evaluation sets.