Digital Forgery Detection for Certificates and Documents Using Image Processing and AI

Abstract

In the digital age certificate forgery and other official documents are a major menace to academic, corporate and legal organizations. Some of the traditional manual forms of verification can be inefficient, time consuming and subject to human error. The paper presents a Digital Forgery Detection System that combines Image Processing and Artificial Intelligence (AI) to classify elements that might be manipulated in digitized documents with high accuracy and is automated.
The multi-staged framework used in the system has Optical Character Recognition (OCR) to analyze text and extract patch-level features as a way of identifying structural inconsistencies. The model can detect tampered signatures, logos, and seals specifically by employing Convolutional Neural Networks (CNNs) and feature-matching algorithms. The result is a forensic report that points to the existence of manipulated areas and a conclusive authenticity rating.
The architecture takes advantage of a hybrid architecture in the detection of minute pixel-level anomaly, including copy-move or splicing attacks, which are not always noticeable by the naked eye. Image inpainting can also be combined to enable the system to rebuild original backgrounds which can serve as a base to detect disturbed text or foreign objects. This two-layered verification provides that even advanced forgeries that imitate original fonts or textures are correctly identified as such.
Comparison of the algorithm with a wide range of real and fake certificates shows that the CNN-based classification is more accurate in comparison with traditional threshold-based algorithms. The system can be scaled and be depended upon by institutions to provide an objective, quantified authenticity score which can be used in institutional security. This study fills the void between forensic image and real-life document authentication and provides a strong defense against digital fraud.