Enhanced Hybrid Ensemble Framework for Deepfake Image Detection

There are many images that are widely viewed in the digital era and people often post numerous images on the social media and online platforms. The current developments in generating AI technology have facilitated advanced manipulation of these pictures, especially using deep fake technology, which has the ability to manipulate the real face with an artificial face that is convincingly real. This is a highly realistic deepfake that poses considerable threats to the security of digital media, Privacy, and trust. In most cases, deepfakes prove those who engage in actions they did not perform in reality. In the recent past, there have been instances to slander political leaders, blackmail, and falsify information as also pretend actions, such as fake terrorist activities. To overcome these hurdles, there should be efficient detection systems. We have developed a hybrid architecture applying the deepfake database to detect deepfake photos. This hybrid framework is an ensemble of Machine Learning (ML) and Convolutional Neural Network (CNN or ConvNet). The model examines both the finer details at the pixel-level up to the entire patterns to reveal a deepfake images. We have used image preprocessing method before training, to enhance the quality of the image and focus on small inconsistencies. Notably, the model uses Explainable AI (XAI) to visualize the facial regions affecting its binary classification. That means it is more understandable. Such openness helps to reconcile confidence in the decisions made in the model. Altogether, the proposed paper will help to properly define deep fake images through a hybrid solution, which is the combination of detection abilities and explainability and provides a solution to the growing threat of image manipulations.