A Novel Approach on Detecting Deep Fake Imagery in Images and Videos

Advances in artificial intelligence have made deep fake technologies develop very rapidly, with tremendous requirements for detection systems that guarantee efficiency in maintaining digital integrity. This paper is an approach that outlines the use of state-of-the-art neural networks in the development of a multimodal framework of selecting deepfakes with precise and efficient identification. The framework will be a spatial feature extraction through EfficientNetB7, spatial-temporal analysis using 3D Convolutional Neural Networks, and sequential learning applied for temporal data using the LSTM network. The new approach is sensitive to micro-level anomalies, including visual artifacts, inter-frame dynamics, and long-term temporal relationships that uniquely characterize synthetic media from actual information. This hybrid strategy offers an integrated solution in the battle against emerging techniques of synthetic media creation exploiting multiple analytical skills. Beyond providing a solution to technical problems of deep fakes, the paper contributes to larger social projects toward regaining trust in digital content. The results demonstrate how multimodal architectures could be transformative for the domain of digital forensics thereby opening new fields to new applications in the fight against misinformation.