• N.Vamshi Krishna
• M.Kavya Sri
• K.Nandu
• G.Sai Krishna

It might have been unthinkable to predict the popular distribution of deep learning to address such complicated difficulties even very few decades earlier. Technology has numerous beneficial implications, but it may also be abused to damage our community. Memes are one such issue, and today greater than ever, when anybody with a cellphone app can generate a fake video or picture, it's critical to take steps to verify online material. Human brains may be used to create auditory and visual "deepfakes." Spoofing attack uses Parametric Antagonistic Systems to simulate rival algorithms for machine learning (GANs). Numerous well- known individuals have already been impacted by the quick propagation of fake news articles on social media networks like Facebook, Google, and Twitter. Twitter, for example. Before being reviewed, artificially generated neural network deep fakes may seem like actual photos or movies, yet consistently leave away telltale time and space traces. A human brain that has been trained to concentrate in Strong fake detecting will be capable of recognizing these indications fast, even though they may not be visible to the naked eye. In this assignment, we will use Recurrent System and InceptionResNetV2 to identify whether or not a picture is a deep fake.

"DEEP FAKE DETECTION USING ADVANCE CONVNETS2d", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.g42-g45, March-2023, Available :http://www.jetir.org/papers/JETIR2303608.pdf

"DEEP FAKE DETECTION USING ADVANCE CONVNETS2d", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppg42-g45, March-2023, Available at : http://www.jetir.org/papers/JETIR2303608.pdf