Unmasking the digital deception - a comprehensive survey on image forgery and detection techniques.

In today's digital landscape, digital images are widely used for communication across various platforms, but they are also vulnerable to tampering and deception. The proliferation of image editing tools and software has made it easier for individuals to manipulate images, but extensive tampering can compromise the accuracy of conveyed information. The human eye is difficult to distinguish between original and tampered images, leading to potential misinformation. Therefore, robust mechanisms using powerful algorithms are crucial to examining image authenticity and detecting forgery. This is especially important in fields like forensics, where evidence manipulation can have significant implications. This work conducts a survey providing a structured overview of research on image forgery techniques and detection approaches. The survey discusses various approaches, including source camera identification, type-dependent and type-independent methods, and recent deep-learning approaches. The survey also presents benchmark datasets for training and validation. The survey aims to improve understanding of research paths in this field and demonstrate how techniques initially developed for a domain can be applied to other areas. It aims to assist other researchers in enhancing their expertise in digital forensics and forgery detection techniques. [ABSTRACT FROM AUTHOR]