DEEPFAKE FORENSIC SUITE- IDENTITY MAPPING & MEDIA INTEGRITY VERIFICATION SYSTEM

Abstract

In the contemporary digital landscape, the exponential proliferation of high-dimensional multimedia data across social platforms, communication networks, and biometric authentication channels is accompanied by an escalating threat of sophisticated generative deception. Deepfakes and synthetic media manipulations present critical systemic risks, ranging from targeted identity fraud to widespread misinformation campaigns. Traditional forensic methodologies—such as pixel-level error level analysis, lighting inconsistency checks, and static rule-based verification—fail to scale efficiently against modern deep synthesis techniques due to heavy compression assumptions, manual feature-engineering constraints, and computational latency. To address these challenges, this monograph presents the design and deployment of the Deepfake Forensic Suite, an automated Identity Mapping and Media Integrity Verification System. The proposed framework establishes a multi-layered security pipeline. First, it implements a high-precision biometric mapping and alignment phase utilizing Multi-task Cascaded Convolutional Networks (MTCNN) to isolate facial regions and eliminate environmental noise. Second, it leverages an optimized MobileNetV2 architecture to extract deep spatial features and compress complex visual attributes into a compact latent representation. By learning the structural characteristics of authentic human faces, the system computes principled prediction probability scores that naturally diverge when processing synthetic manipulations. Furthermore, a statistically robust tri-state classification strategy (Real, Fake, or Uncertain) is established based on validation-set confidence percentiles, enhancing forensic reliability by flagging borderline cases for manual administrative review. The performance of the system is evaluated against established baselines, including traditional Viola-Jones frameworks and shallow convolutional structures. Finally, the practical deployment-readiness of the system is demonstrated through model serialization, a real-time webcam inference API, and a reproducible, interactive web dashboard engineered entirely within the Streamlit framework. The resulting suite provides a lightweight, high-assurance digital forensics solution capable of edge-device execution without requiring slow, cloud-dependent infrastructure.