International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 1 January-March 2026 Submit your research before last 3 days of March to publish your research paper in the issue of January-March.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 1
January-March 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI-Powered Forensic Analysis System for Automated Certificate and Document Authentication

Author(s) Ms. Sai Harika Harika Boya chirappagari, Mr. Naresh Boggu, Mr. Kamalakannan k, Ms. Swapna kuruba
Country India
Abstract The integrity of professional and legal transactions relies heavily on authentic certificates. However, the proliferation of sophisticated digital manipulation tools has made it easy to create fraudulent documents that are indistinguishable to the human eye. Current manual verification processes are labor-intensive, slow, and prone to human error, creating significant bottlenecks. Standard automated tools like QR code verification often fail to detect visual anomalies or sub-pixel discrepancies in the document's actual content. This project proposes an automated web-based forensic tool designed to identify document tampering at a sub-pixel level. By integrating Error Level Analysis (ELA) via OpenCV to highlight JPEG compression inconsistencies and a specialized Convolutional Neural Network (CNN) for high-precision classification, the system targets a detection accuracy of over 95%. Furthermore, Explainable AI (XAI) heatmaps are generated using Grad-CAM to provide transparent visual evidence of the specific regions contributing to the forgery decision. It is observed that this dual-layered approach significantly enhances the reliability of automated document authentication in academic and administrative sectors.
Keywords Artificial Intelligence, Certificate Authentication, Convolutional Neural Networks, Document Forensics, Error Level Analysis, Explainable AI, Image Forgery Detection, Machine Learning.
Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-29
View / Download PDF File

Share this


CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 17 Isu 1Cover Page Vol 16 Isu 3Cover Page Vol 16 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org