Image Forgery Detection Using Machine Learning

With the widespread availability of various powerful image editing softwares, the integrity of digital content has become increasingly vulnerable to manipulation and forgery. Image forgery is a concept where images are edited/modified and often mislead people to make assumptions. This is a pervasive issue in today's digital age, with the advancements in image editing tools making it increasingly difficult to detect manipulated images. To tackle this problem, a model has been developed to analyze and classify images as either authentic or forged by leveraging the power of deep learning algorithms. The model achieves an impressive accuracy of 93.39%, making it highly reliable for practical applications. The model has been developed by using algorithms such as Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs). The user has to enter the path of an image as the input and then the model employs its learnt features and patterns to evaluate the authenticity of the image provided. The research paper describes the methodology adopted for training the model, including the dataset used and the deep learning architecture employed.