Detecting Subtle Deep Fake Audio and Image using Transfer Learning

Multimedia forensics has advanced rapidly in detecting manipulations in multimedia content mainly because of the integration of the recent deep learning methods current systems depend predominantly on deep neural networks DNN. DNN performs very well at the detection of phony audio but suffers from low prediction accuracies and is incapable of conducting images for manipulation to overcome these shortcomings we propose a hybrid approach using recurrent neural networks RNN-this model is is made to predict both audio and image manipulation effectively as it accommodates the temporal dynamics of audio data while using image processing capabilities in our proposed system this way it allows trainings separately audio and image data thus giving a specialized and accurate detection process in addition there is a web application implemented which is developed using bootstrap that serves to give an easy and intuitive interface by which users will look to analyze multimedia content with the aid of RNN the system achieves better accuracy in predicting both manipulated audio and manipulated images thereby enhancing the overall effectiveness of multimedia forensics the proposed approach expands the detection realm while opening scope for future research in the area and ensures that a solid framework continues to be in place for multimedia manipulations to be identified in an ever-changing digital environment