Image Forgery Detection and Deep Learning Techniques: A Review
Ritu Agarwal, Deepak Khudaniya, Abhinav Kumar Gupta, Khyati Grover · 2020
Due to the easily available software for tampering images, image manipulation has become quite common. Since the tampered images are non-distinguishable by the naked eye, they are being circulated on various platforms giving rise to rumors and misleading many. This has led researchers to work on various techniques for the detection of manipulated images with improved accuracy. Traditional works on image forgery detection are mostly based on extracting simple features that are specific for detecting some particular type of forgery. Recently, works on forgery detection based on neural networks have proved to be very efficient in detecting image forgery. Neural networks are capable of extracting complex hidden features of an image, thus giving better accuracy. Contrary to the traditional methods of forgery detection, a deep learning model automatically builds the required features, hence it has become the new area of research in image forgery. The paper initially discusses various types of image forgery techniques and later on compares different approaches involving neural networks to identify forged images.