Tampering of digital photos or images is known as image forgery. The ability to create phony images or information has become easier due to the rapid advancement of technology. In order to detect image forgeries, this paper proposes a model that employs Error Level Analysis (ELA) with Convolutional Neural Networks (CNN). ELA is used as a preprocessing step to highlight regions of an image that may have been tampered with. CNN is then trained on this enhanced data to classify images based on their authenticity and detect digital modifications. This initiative’s main goals include image classification, attribute extraction, image authenticity verification, and digital image modification detection. Our suggested solution makes use of CNNs’ deep learning capabilities and the refinement found by ELA.