Implementation of Deep Learning Method for Forgery Detection on Social Media

In recent years, the surge in misinformation and rapid technological advancements has significantly increased the prevalence of media manipulation. The advent of AI-altered videos and sophisticated news content poses a serious threat to media integrity, particularly as these manipulations proliferate on social media platforms, creating challenges in discerning authenticity. The accessibility and user-friendliness of deepfake technology have compounded the issue, making the distinction between genuine and fabricated content increasingly challenging. This presents substantial risks, ranging from the dissemination of false information to fostering a general sense of scepticism toward online visuals. This research aims to comprehensively analyze the process of creating deepfakes and assess their broader societal impact, while also proposing potential solutions to mitigate this problem. The methodology employed has achieved accuracy of 87% that involves utilizing ResN ext, a CNN architecture with LS TM, to analyse fake videos, and error level analysis followed by CNN algorithm to analyse fake images, this research outlines the specific steps and procedures involved in this analytical process.