Deepfakes are realistic, human-synthesized videos that are incredibly simple to produce thanks to the advent of sophisticated algorithms fueled by advances in the field of deep learning. Deepfakes are being used to create fictitious news stories about terrorism, politics, and retaliation in order to incite societal unrest. The development of efficient techniques for identifying deepfakes is imperative, given the mounting concerns surrounding them. Our work in this field offers a brand-new deep learning-based method that effectively distinguishes between authentic videos and artificial intelligence-generated phony ones. Our technique can identify deepfakes that are both reenactments and replacements. To counter the threat posed by artificial intelligence (AI), we suggest a system that makes use of AI. In order to train an InceptionV3 model to categorize films as either real or manipulated, depending on whether they have undergone any kind of alteration, this method uses an MTCNN neural network to extract frame-level information. We assess our method on a large-scale balanced and mixed data set in order to mimic real-time scenarios and improve the model’s performance on real-time data. This dataset was painstakingly created by combining multiple available datasets.