TrueVision: Image Forgery Detection with Deep Learning Techniques

The rapid growth of digital image manipulation tools has significantly increased the prevalence of image forgeries, posing serious challenges to media credibility, digital forensics, and information security. This project presents a deep learning–based image forgery detection framework designed to identify and classify tampered images with high reliability. The proposed approach employs convolutional neural network (CNN) architectures to automatically learn discriminative features from forged and authentic images, eliminating the need for handcrafted feature extraction. The model is trained and evaluated on a labelled dataset containing both genuine and manipulated images, including common forgery types such as copy–move and splicing attacks. Experimental results demonstrate that the deep learning–based method achieves superior accuracy and robustness compared to traditional image forensics techniques. Additionally, visual explanation methods are used to highlight manipulated regions, improving interpretability and trust in model decisions. The proposed system shows strong generalisation capability and offers an effective, scalable solution for real-world image forgery detection applications.