Comprehensive review on digital image forgery detection methods
Nishigandha N. Zanje, Gagandeep Kaur, Anupkumar M. Bongale · 2023
Owing to the current times of quick advancement, several methods used to create and alter digital information today offer an extremely high level of authenticity. The line separating authentic and artificial content has shrunk significantly. It presents serious cyber security risks. Anybody can produce incredibly believable bogus photos using software applications that are readily accessible online. Such fake images can be utilized to defraud individuals, defame, or extort someone, as well as to manipulate public perception about certain individuals amid campaigns. Automated systems which can identify fake digital content are thus urgently needed in order to limit the circulation of harmful misleading data. The purpose of this survey work is to explore Digital Image Forgery Detection (DIFD) methods. The new trend of Deepfakes, fake multimedia produced using deep learning algorithms, as well as contemporary data-driven forensics techniques to combat them will receive close attention.