Authors - Morveen Bamania, Anilkumar Patel, Yassir Farooqui Abstract - Digital image manipulation has become sophisticated day by day with the help of advanced editing tools. This posing significant challenges to image authenticity verification and raising a critical concern in the field of legal proceedings, social harmony, scientific publications, forensic and law enforcement, healthcare and journalism. In this paper we implement a unique and novel approach for the detection of image forgery. We use Convolutional Autoencoder (CAE) combined with Error Level Analysis (ELA). Our proposed preprocessing pipeline follows the sequence: resize the input image and pass through ELA apply denoise method. Where Gaussian denoising is strategically applied to the ELA output rather than the original image to preserve forgery artifacts while reducing noise. The CAE architecture consists of a four-block encoder that compresses input images into a 128- dimensional latent space, a symmetric decoder for reconstruction, and a fully connected classifier for binary forgery detection. The model is trained using a combined loss function. One is Mean Squared Error (MSE). It helps for reconstruction. The other one is Binary Cross- Entropy (BCE). It improves its ability to correctly classify. Experimental evaluation on the CASIA v2.0 dataset demonstrates the effectiveness of our approach. It is achieving competitive accuracy, precision, recall, and F1-score metrics. The proposed method successfully identifies both copy-move and splicing forgeries. It identifies the forgeries by analyzing compression artifact inconsistencies revealed through ELA.
Open Zoom