The developing computation control has made the profound learning calculations so powerful that making an unclear human synthesized video famously called a profound fake has got to be exceptionally straightforward. Scenarios where these practical confront swapped profound fakes are utilized to form political trouble, fake psychological warfare occasions, vindicate porn, and shakedown people groups are effortlessly imagined. In this work, we depict a modern profound learning-based strategy that can viably recognize AI-generated fake recordings from genuine videos. Our strategy can naturally be recognizing the substitution and reenactment of deep fakes. We are attempting to utilize Manufactured Intelligence (AI) to battle Fake Intelligence(AI). Our framework uses a res-next neural convolution system to extract frame-level highlights and promote the use of these highlights to prepare the long-term memory (LSTM)-based repetitive neural network (RNN) to classify whether the video is subject to art. control or not , i.e whether the video is profoundly fake or genuine. To imitate the genuine time scenarios and make the show perform way better on genuine time information, we assess our strategy on an expansive sum of adjusted and blended data-set arranged by blending the different accessible data-set like Face-Forensic, Deep Fake location challenge, and Celeb-DF. We moreover focus on how our framework can accomplish competitive results utilizing exceptionally straightforward and strong approaches
