Video Deepfake Detection System Using Deep Learning

This comprehensive study delves into the dynamic geography of deep literacy operations, fastening on the burgeoning realm of deep fakes. Deep literacy has seamlessly integrated into fields like natural language processing, machine literacy, and computer vision, giving rise to innovative operations. still, the swell in deep fakes, sophisticatedly manipulated videos images, has come a pressing concern. The unrighteous operations of this technology, similar as fake news, celebrity impersonations, fiscal swindles, and vengeance porn, pose significant pitfalls in the digital realm. Particularly, public numbers like celebrities and politicians are largely susceptible to the Deep fake discovery challenge. This exploration totally assesses both the product and discovery aspects of deep fakes, employing different deep literacy algorithms, including InceptionResnetV2, VGG19, CNN, and Xception. The evaluation is done by using Kaggle deep fake dataset, highlights Xception as the most accurate among the algorithms studied. As vicious uses of deep fakes escalate, the imperative for robust discovery mechanisms intensifies to guard against implicit societal consequences