Image Forgery Detection using CNN And Siamese Networks

Image forgery detection is a critical challenge in digital forensics, requiring advanced techniques to identify tampered regions in digital images. This research presents a novel deep learning approach that integrates Convolutional Neural Networks (CNNs) with Siamese Networks for precise forgery detection. The proposed CNN-Siamese model extracts fine-grained image features and evaluates patch similarities to detect and localize forged regions effectively. Utilizing an encoder-decoder structure, the model enhances feature extraction and similarity assessment for improved accuracy. The system is extensively tested on diverse datasets covering multiple forgery techniques and manipulation environments. Experimental results demonstrate that the CNN-Siamese model achieves a low Mean Squared Error (MSE) of 0.0003, significantly outperforming conventional methods, including a standalone CNN model (MSE = 0.011) and an RNN-based approach (MSE = 0.0004). These findings establish the CNN-Siamese architecture as a highly reliable solution for image forgery detection, advancing the field of digital forensics.