Design of an Integrated Model for Long-Distance Attention and Multi-Modal Trajectory Analysis in Deepfake Video Detection Operations

Abstract

More realistic and frequent deepfake films require computational methods to detect minor temporal and behavioral problems rather than visual hints. Compression noise, lighting variations, and low-resolution uploads muddy short-range visual artifacts, making detection systems difficult and attention models that just observe local frames unlikely to discover long-term anomalies Most documented techniques lack biomechanical knowledge, which may weaken their reactions to well-trained generative models. This sequential detection architecture uses Temporal Long-Distance Attention Memory Encoding, Semantic Frequency Cluster Analysis, Facial Motion Vector Diffusion Tracking, Cross-Modal Residual Alignment Networks, and Adaptive Anomaly Trajectory Regression. Global context builds from each level. In contrast to nature, long-distance attention recorded frame separations with skin tone drift and eye-blink periodicity. Frequency clustering separations face teeth and eyelid spectral abnormalities may imply generative blending. The diffusion tracking of landmark motion identifies micro-gestural acceleration and jitter. Cross-modal alignment compares spoken timbre and consonant shape. Due to deepfakes' distinct wobbling from biomotions, anomaly trajectories assess how these inaccuracies evolve. In videos with occlusions or low contrast, our layered technique decreases false negatives and enhances compression-hardened fake detection. Analysts may visualize subtle inconsistencies with the model's outputs, which may effect future authenticity pipeline work in social media moderation and digital evidence processing.