This project involves the development and implementation of a deep learning model for image forgery detection. The model utilizes a combination of convolutional neural networks (CNNs), autoencoders, and spatial attention mechanisms to enhance its performance in detecting forged regions in images. The model architecture consists of an encoder network that captures high-level features and a decoder network that reconstructs the input image.