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Call For Paper
Volume 12 Issue 07
July 2026
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Abstract
The Rapid Proliferation Of Digital Content Creation Tools, Artificial Intelligence Platforms, And Advanced Image Editing Software Has Significantly Increased The Risk Of Digital Forgery In Images, Documents, And AI-generated Media. Traditional Forensic Methods Are Limited To Single-domain Analysis And Lack Integration, Centralized Evidence Management, And Automated Reporting. This Paper Presents, A Hybrid Digital Forgery Detection Framework Designed As A Unified, Full-stack Digital Forensic Intelligence Platform. The System Integrates Three Specialized Forensic Detection Modules: (i) Copy-Move Image Forgery Detection Using The Scale-Invariant Feature Transform (SIFT) Algorithm With FLANN-based Matching, (ii) Document Forgery Detection Using OCR-based Text Consistency Analysis And Structural Validation, And (iii) AI-Edited Image Detection Using Error Level Analysis (ELA), Noise Residual Analysis, And Compression Artifact Examination — All Within A Single Centralized Dashboard. The Platform Is Developed Using Next.js/TypeScript For The Frontend, FastAPI/Python For The Backend, And SQLite For Database Management. Experimental Evaluation Confirms Successful Execution Of Multi-category Forensic Analysis, Dashboard History Tracking, And Structured Forensic Report Generation With Tampered Region Localization. The Modular Architecture Supports Future Extensions Including Deepfake Video Detection, Blockchain-based Evidence Preservation, And Cloud-based Deployment.
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Paper ID
IJSARTV12I5105518
Publication Date
May 26, 2026
Research Area
Computer Application