Digital Image Forgery Detection

In today’s digital age, social media is image-dominated, making visuals a central tool for communication and dissemination of information. This extensive usage of images, however, has also given rise to their malicious exploitation by means of intentional manipulation, mostly in the form of producing fictitious or false content. Image forgery—modifying images to deceive audiences—has emerged as a key problem, perpetuating the spread of misinformation, cyberattacks, and even legal issues. In response to the increasing problem, deep learning algorithms have become an effective solution in identifying fake images. Specifically, Convolutional Neural Networks (CNNs), a branch of deep learning algorithms, are very effective at identifying subtle inconsistencies in images. By conducting pixel-level analysis, CNNs can discover unnatural visual patterns, tiny inconsistencies, and indications of manipulation that go unnoticed by the human eye. CNNs are efficient at detecting prevalent forms of image forgery like splicing, copy-move, and retouching, by examining prominent image features such as edges, texture, and lighting irregularities. Out of numerous techniques, the combination of Error Level Analysis (ELA) with CNN structures has emerged with tremendous potential for detecting tampered areas. CNNs can learn from very large datasets including original and tampered images and make accurate classifications and localizations of forged content. Therefore, deep learning—specifically CNNs in conjunction with ELA—has been a very effective solution to counter the growing problem of image forgery, thus improving credibility and reliability in digital media.