Comprehensive study on image forgery techniques using deep learning

Abstract

Image forgery detection using deep learning is a technique that utilizes artificial neural networks to identify whether an image has been morphed or manipulated. This is an important task in the digital age, as the proliferation of easily accessible photo editing software has made it easy for individuals to alter images for various purposes, such as spreading misinformation or altering evidence. Deep learning algorithms work by training on large datasets of images and learning to recognize patterns and features that are indicative of forgery. These algorithms can then be used to analyze new images and determine whether they have been manipulated. One approach to image forgery detection using deep learning is to train a convolutional neural network (CNN) on a dataset of both genuine and forged images. The CNN can then be used to classify new images as either genuine or forged based on the features it has learned to recognize. An additional strategy is to make use of a generative adversarial network, often known as a GAN. This system is made up of two neural networks that collaborate with one another to generate and categorise images. By training, the GAN on a dataset of genuine and forged images, the discriminator network can learn to recognize the features of forged images and classify them accurately. There are several challenges to overcome when using deep learning for image forgery detection. Despite these challenges, deep learning has shown promising results for image forgery detection and is an active area of research. In the future, it is likely that deep learning algorithms will play an important role in detecting and preventing image forgery, helping to maintain the integrity and trustworthiness of digital images.