REVIEW PAPER ON DEEP FAKE DETECTION USING DEEP LEARNING - Singh, Shikha; Khan, Shavez; Pandey, Chandan; Sahani, Sandhya; Singh, Abhishek; Patel, Vivek (International Journal of Research -GRANTHAALAYAH, 2025)
In the modern digital age, the emergence of artificial intelligence has made it possible to create hyper-realistic human faces that do not exist in reality. Such AI-generated images, popularly referred to as deepfakes, pose an increasing threat as they can fool the human eye and be used for nefarious activities. The study centers on the identification of such forged images via deep learning, that is, CNNs. Unlike the majority of current research that mainly focuses on video-based deep fake detection, our method tackles the problem at image level. This is important, as even our one single doctored image can be used to spread false information, impersonate others, or breach security mechanisms. We built a detection model able to pick out subtle artifacts and inconsistencies-numerically, in many cases invisible to the naked eye-performed by the process of generating an image. Our intention is not only to design and performative model but also help strengthen the general attempt at restoring and sustaining trust within digital material through developing available and precise fake image detection.