A Comparative Study for Analyzing the Impact of Spatial-Temporal Preprocessing on Deep Fake Detection

With advanced techniques making it harder and harder to discern between authentic and fraudulent information, deepfake technology has become a major problem for digital media. This research provides a thorough comparative analysis of deepfake detection systems, emphasizing how spatial-temporal preprocessing strategies might improve detection accuracy. This study attempts to assess how various preprocessing techniques impact the effectiveness of deep learning models in spotting deep-fake content by using both spatial and temporal information from video sequences. Various advanced spatial-temporal preprocessing methods are implemented and contrasted on deep-fake video datasets, analyzing crucial parameters like detection precision, computational effectiveness, and resilience against hostile assaults. The findings provide insights into the best preprocessing techniques for deepfake analysis by showing how particular combinations of spatial-temporal variables dramatically increase detection performance. This work adds significantly to the area of digital forensics by providing a more thorough knowledge of how spatial-temporal preprocessing might improve the identification of deeply false material.