Deep Fake Image and Video Detection using Deep Learning

In today's modern world, nothing can be trusted with a sight of an eye. Things which we observe might be a false content. Deep fake is kind of the problem which stands for the state of matter, that the Images or videos which we have seen might be a false content. The multiplication of profound phony pictures and recordings presents huge difficulties to the genuineness and dependability of visual substance on the web. We propose a clever methodology for recognizing profound phony pictures and recordings utilizing profound learning strategies. In particular, we influence the Meso4 engineering for picture-based discovery and LRCN networks for video-based recognition. The Meso4 network proficiently catches unpretentious curios and irregularities normal for profound phony pictures, while LRCN networks successfully model fleeting conditions in video groupings to perceive controlled content. Through broad trials on benchmark datasets, we show the viability of our proposed approach in precisely distinguishing profound phony substance across the two pictures and recordings, outflanking cutting edge strategies. Furthermore, we explore the potential applications of our deep fake detection system in real-world settings, including social media platforms, news organizations, and law enforcement agencies. By integrating our detection system into existing content moderation pipelines, we can enhance the integrity and trustworthiness of visual content online, thereby mitigating the spread of misinformation, preserving individual privacy, and safeguarding societal trust in digital media. In conclusion, our proposed approach addresses a huge headway in the field of profound phony identification, offering a hearty and versatile answer for recognizing and battling the multiplication of controlled visual substance across the web.