Efficiently Identifying Fake Audio and Images Using Transfer Learning

Over the years, the field of multimedia forensics has made great strides in the detection of manipulation of digital content, mainly facilitated by advancements in deep learning. Nevertheless, state-of-the-art systems with DNN come with severe limitations, including very low prediction accuracy for fake audio as well as being unable to analyze manipulated images. We therefore suggest adoption of a hybrid approach using the RNN. Designed to detect accurately whether an audio or image has been manipulated, using that captured temporal dynamics of audio and image processing techniques. Our system trains the audio and image data separately to make a more specific and precise detection. We also developed a friendly web application using Bootstrap, so it can be easily analyzed by users for multimedia content. With integration of RNN in our system, the RNN achieved a high degree of accuracy in both fake audio detection and manipulated images, elevating the general performance in the multimedia forensics domain. This enhances not just the detection ability but also leaves an open door to further research on this subject within a strong framework, for identification of multimedia manipulation within a growing complex digital landscape.