AI Based Document And Image Tampering Detection System

Author(s)	Naveen S, Lovipriyan T, Sanjai K, Kathirvel K, Revathi R
Country	India
Abstract	Widespread adoption of digital documentation has considerably elevated the risk of content manipulation, exposing critical domains such as finance, law, and academic credentialing to sophisticated forgery threats. Conventional detection frameworks, which examine textual or visual content independently and yield only binary authenticity verdicts, fall short in addressing the nuanced nature of modern tampering. This paper introduces an AI-driven tampering detection framework that unifies five analytical components: Text–Image Semantic Mismatch Detection, Cross-Document Tampering Correlation, Intent-Based Tampering Classification, a Self-Learning Adaptation Module, and Legal-Grade Explainable Reporting. Leveraging deep learning, natural language processing (NLP), and incremental learning strategies, the system achieves a semantic consistency accuracy of 94.2% and an overall detection accuracy of 95.8%, demonstrating marked improvements over existing approaches in both forensic interpretability and operational reliability.
Keywords	Document Tampering Detection, Semantic Mismatch Analysis, Explainable Artificial Intelligence, Cross-Document Correlation, Adaptive Learning
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-16

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