DeepShield: A Hybrid Deep Learning Framework For Real-Time Deepfake Detection Using Spatial And Temporal Cues

Deep learning has demonstrated remarkable success in solving complex problems across various domains, such as big data analytics, computer vision, and human-level control. However, the same advancements in deep learning have also given rise to applications that pose threats to privacy, democracy, and national security. One such application is deepfake technology, which leverages deep learning algorithms to create convincingly realistic fake images and videos that are indistinguishable from authentic ones. Consequently, the need for technologies capable of automatically detecting and assessing the integrity of digital visual media has become imperative. This paper aims to present a comprehensive survey of the algorithms employed to create deepfakes and, more importantly, the methods proposed in the literature for detecting deep fakes. The survey delves into extensive discussions on the challenges, research trends, and future directions concerning deepfake technologies. By reviewing the background of deepfakes and examining state-of the-art deepfake detection methods, this study provides an inclusive overview of deepfake techniques, thereby facilitating the development of novel and robust methods to combat the increasingly sophisticated deep fake threats in conclusion, this survey paper provides a comprehensive overview of deepfake techniques and detection methods. By synthesizing the existing literature and highlighting research trends and challenges, it aims to support the development of novel and effective approaches to combat the growing threat of deep fakes, ensuring the integrity, privacy, and security of digital visual media in an increasingly complex and interconnected world.