VeriMedia: A Spatial-Temporal Deep Learning Platform for Video Deepfake Detection and Forensic Verification

The increase of hyper-realistic generative models, deepfakes, face-swapping algorithms, and neural rendering has severely compromised public trust in digital video media, helping centered political disinformation, financial fraud, executive impersonation, and non-consensual synthetic defamation, in many respects; synthetic artifacts are clearing heterogeneously across the microscopic pixel textures and inter-frame temporal transitions, when the facial video undergoes generative forgery, in real-world scenarios.Standard digital forensic workflows rely on static two-dimensional Convolutional Neural Networks (2D-CNNs) that are inspecting isolated frames; in fact, yet, frame-by-frame classifiers are fundamentally blind to temporal boundary flickers, physiological inconsistencies (such as irregular eye blinking flow and micro-expression dynamics), and motion discontinuities.Along these dimensions, suffering from acute vulnerability to lossy social media compression (such as H.264/H.265 codecs at high quantization parameters c23 and c40); in fact, also black-box deep models output uninterpretable scalar scores without spatial or temporal attribution, restricting their evidentiary admissibility in judicial and regulatory environments.Current methods across the digital media forensics, deep spatial representation learning, temporal sequence modeling, and explainable artificial intelligence (XAI) are reviewed by this survey paper, broadly speaking.In parallel with this, forensic feature acquisition mechanisms (handcrafted biological heuristics, frequency-domain changes, and deep convolutional backbones), temporal sequence learning pipelines, and emerging explainability frameworks, and benchmark leading industry platforms (Microsoft Video Authenticator, Intel FakeCatcher, Sensity AI) alongside academic systems (MesoNet, XceptionNet, EfficientNet-B4, C3D/I3D, LipForensics, FTCN are gauged), on the whole.To tackle key research gaps around temporal blindness, compression sensitivity, and ungrounded black-box outputs, we suggest the architectural design of VeriMedia, namely one full-stack, spatial-temporal forensic framework combining zero-trust video ingestion, MTCNN facial landmark alignment, any ResNeXt-50 (32x4d) clustered residual convolutional backbone, a further multi-layer Bidirectional Long Short-Term Memory (Bi-LSTM) sequence modeling pipeline with dual-threshold confidence gating, and a dual-stream visual explainability engine delivering spatial Grad-CAM saliency heatmaps and temporal anomaly waveforms anchored by a tamper-evident cryptographic SHA-256 PDF dossier.Finally, open research challenges and position multimodal audio-visual synchronization are analyzed as a planned future extension.