A Deep Learning Framework for Deepfake Detection and Digital Media Protection with Explainable Forensic Verdicts and Provenance Watermarking

Abstract: The rapid democratization of generative synthesis tools has made hyper-realistic manipulated images and videos, commonly termed deepfakes, trivially easy to produce and disseminate, posing serious threats to identity, reputation, journalism, and public trust. Conventional manual verification cannot scale to the volume and sophistication of such content, and many automated detectors offer a binary label without interpretable evidence or any downstream protection of authentic media. This paper presents a deep-learning framework that not only classifies digital media as authentic or manipulated but also produces explainable forensic verdicts and applies provenance safeguards to genuine content. The proposed system fuses spatial convolutional features with frequency-domain and temporal-inconsistency cues, generates region-level manipulation heatmaps for interpretability, and embeds an invisible watermark together with a logged provenance hash for verified media. A Python back end implements model inference and forensic analysis, while a Node.js layer delivers an analyst-facing dashboard. Evaluated against handcrafted-feature and single-stream convolutional baselines, the framework attained approximately 94% accuracy and an area under the ROC curve of 0.96, with balanced precision and recall. The principal contributions are a multi-cue detection pipeline that improves robustness over single-stream models, an explainability component that surfaces where manipulation is suspected, and an integrated protection mechanism that links detection to media authentication.

Keywords: Deepfake detection; digital media forensics; convolutional neural networks; explainable AI; image and video authentication; watermarking; media provenance.