RealPratham21/Digital-Image-Forensics-Framework: Hybrid Real-Time Image Tampering Detection & Localization — Reproducible Research Release v1.0.0

This release corresponds to the first reproducible research version of our hybrid framework for real-time digital image tampering detection and localization. The release is archived for research reproducibility and DOI generation via Zenodo. Included in this Release Complete hybrid detection framework (ViT + EfficientNet + LightGBM + YOLOv8-Seg) Training and inference pipeline Confidence-weighted ensemble fusion Conditional localization logic Experiment notebook used for final results Dataset preprocessing and evaluation scripts Reproducibility configuration and environment notes Experimental Environment GPU: NVIDIA Tesla T4 (Google Colab Free Tier) Python 3.10 CUDA 12.x PyTorch / TensorFlow GPU builds Datasets Used CASIA v2 — Copy-Move and Splicing Inpainting Localization Dataset Deepfake and Real Images Dataset JPEG / Seam-Carving Forgery Dataset EXIF Metadata Manipulation Dataset (Dataset links are provided in README) Purpose of this Release This release is created to: Ensure full reproducibility of the research Freeze the exact version used in the paper Generate DOI via Zenodo for academic citation Provide transparent access to code and experiments Notes This release represents the stable research version used for publication Future improvements and experiments may appear in later releases Pretrained weights may be added in a future release depending on dataset licensing Citation If you use this repository, please cite: Kamble, V.B., Uke, N.J. Hybrid Framework for Real-Time Detection and Localization of Digital Image Tampering (Under submission / 2026) Contact Vitthal B. Kamble Research Scholar — VIIT Pune Email: vitthalk13@gmail.com