“Combatting Deceptive Media : An In- depth disquisition of Machine Learning ways for relating Fake Multimedia Content”

Abstract: With the rapid progress of technologies like Artificial Intelligence (AI), deep learning, and Machine Learning(ML), the ability to manipulate and modify images has become more accessible. This has led to the emergence of a concerning phenomenon known as deepfakes, where criminals can generate deceptive videos, images, or audio content. In addressing this growing challenge, the present paper introduces implementation of numerous approaches including Residual Networks (ResNet), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) and Random forest with reference to identification of fake content. Despite the video, audio and image deepfake detection, this paper also introduces about live creation of the deepfakes.

Keywords: Deepfake, Residual Networks, LSTM, CNN, and Random Forest