Authentica AI: An Explainable Multimodal System for Synthetic Media Detection Using Deep Features, Reconstruction Error and Frequency-Domain Forensics

The rapid improvement of generative models has made AI-generated images, cloned voices and manipulated video increasingly difficult to distinguish from authentic media, while most deployed detectors examine a single modality and return an opaque score. This paper presents Authentica AI, a web-based multimodal synthetic-media detection system that automatically routes each upload to a modality-specific detector. Images are analysed by a pretrained convolutional backbone (EfficientNetB0, with a ResNet-50 variant), a diffusion-reconstruction error map, and five handcrafted forensic signals — spectral energy loss, spatial variation, edge-sharpness consistency, colour-channel correlation and local-variance entropy — which are fused by a weighted ensemble and a calibrated sigmoid into a single synthetic-probability score. Audio is converted to log-Mel spectrograms and classified by a ResNet-18 model. Every image verdict is accompanied by a forensic heatmap that localises the regions that drove the decision, and results are stored in a PostgreSQL database and presented through an interactive dashboard with batch processing. In a prototype evaluation on a five-image batch the system returned verdicts with a mean latency of 263.3 ms per image on a CPU, and per-stage profiling of a single run (288.1 ms) shows that the reconstruction stages account for about two thirds of the processing time. The paper also states the limits of this evaluation: no labelled benchmark accuracy is reported, and large-scale benchmarking and video support are identified as future work.