Developing an Optimal Strategy to Address the Vulnerability of Image Tampering

Abstract

The paper proposes a hybrid image tampering detection system that incorporates the Convolutional Neural Networks into the pool of traditional forensic techniques such as Error Level Analysis and noise analysis. Its objective is to provide high detection accuracy in tampered images through deep learning and forensic methods. According to this method, ELA detects compression inconsistencies in the system, while noise analysis detects abnormal noise patterns in the image. A combination of these techniques provides the capability for the system to capture various methods for tampering, including copy-move forgery, splicing, and subtle retouching. It was trained and tested on the CASIA 2.0 dataset with high accuracy: 98% training accuracy and over 96% validation accuracy. It was successfully deployed as a real-time Flask web application wherein users can upload an image and perform the analysis very quickly. While powerful, the model has a limitation of only revealing a subset of lossless image format tampering and performs subtle manipulations. The future work will involve enhancing scalability and deepfake detection that can handle complex techniques of tampering. The research proposed herein provides a holistic and scalable solution for the detection of image tampering to be applied in digital forensics, verification of media, and cybersecurity