Unmasking DeepFake Visual Content with Generative AI

Recent advances in deep learning-based generative models have increased the proliferation of fake media, which has caused severe unrest globally. These generative models can create ultra-realistic images and videos that are almost impossible to differentiate from traditional image and video processing techniques. As a result, there has been a considerable demand for effective fake multimedia detection methods. This paper provides an in-depth review of different approaches to deepfake to understand and exploit counterfeit media content. The available learning techniques for creating and detecting forensic setups have been investigated in this paper as the authenticity and integrity of multimedia content play a significant role in decision-making or providing verdicts. Ultimately, we point out various futuristic technologies that can rejuvenate research to design a full-proof deepfake ecosystem.