Detecting Image Tampering with Error Level Analysis Using Convolutional Neural Networks

Fake image detection has become an urgent task. While advanced technology benefits us, it also poses a threat when used in cybercrime. Images are often considered solid evidence to prove something concrete, and image falsification or tampering—when done to benefit a party at fault—can result in misleading conclusions. Detecting such manipulation requires a substantial amount of image data and a model capable of processing every pixel to provide accurate and efficient results in real time. To address this, a model that combines Convolutional Neural Networks with Error Level Analysis has been developed, achieving an accuracy of 94% and convergence in 40 epochs. Using this model, a website has been created that allows users to check if an image is original or has been tampered with, and offers the option to store authentic images in a secure environment.