Convolutional Neural Network Based Detection Mechanism for Deepfake Image

The widespread availability of digital smart devices and the rapid growth of multimedia content on social media platforms have created an environment conducive to the manip-ulation of images and videos through advanced machine learning algorithms, notably in the form of deepfakes. These AI -generated multimedia deceptions pose a significant challenge to the verification and integrity of digital media, particularly as they are employed to spread disinformation across social channels. In our current “post-truth” era, deepfakes have emerged as powerful tools for distorting reality, capable of manipulating public opinion, defaming individuals, and even influencing critical events like elections. Deepfakes, categorized into various types such as face-swap, lip-synching, puppet-master, face synthesis and attribute manipulation, and audio deepfakes, present multifaceted threats. Face-swap deepfakes target public figures, distorting scenarios to damage reputations or create false narratives. Puppet-master techniques aim to replicate a person's expressions or entire body movements for deceptive purposes. Therefore, this is the need for an hour to find out about the originality and authenticity of the images. In this research paper, we used convolutional Neural Network and found 92.67% accuracy.