Open Access
Research Paper
Peer Reviewed

Paper Title

Efficient Copy-Move Forgery Detection through Identical Key feature Recognition and Tracing (IKFR-T)

Article Identifiers

Registration ID: IJNRD_216534

Published ID: IJNRD2405129

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Authors

Keywords

Copy move forged detection (CMFD), Image Forgery, Key feature, Identical Key feature Recognition and Tracing (IKFR-T)

Abstract

The practice of copy-move forgery is prevalent in various industries, where images often serve as crucial graphical evidence that can be manipulated using various methods. While machine intelligence has been employed for detecting forged digital images in recent decades, achieving accurate detection remains a challenging task. This research introduces a novel method, IKFR-T (Identical Key Feature Recognition and Tracing), designed for effective copy-move forgery detection in digital images. The methodology involves three key steps: feature extraction, similarity checking, and recursive localization, each optimized for improved performance. Results and discussions highlight the effectiveness of IKFR-T, demonstrating superior performance compared to existing models. Evaluation metrics, including F1-Score, True Positive Rate (TPR), and False Positive Rate (FPR), emphasize the methodology's reliability in detecting copy-move forgeries. The research employs the GRIP dataset, illustrating its applicability to real-world scenarios and providing insights into potential advancements and challenges in copy-move forgery detection.

How To Cite (APA)

Mohan D N (May-2024). Efficient Copy-Move Forgery Detection through Identical Key feature Recognition and Tracing (IKFR-T). INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), b182-b198. https://ijnrd.org/papers/IJNRD2405129.pdf

Issue

Other Publication Details

Paper Reg. ID: IJNRD_216534

Published Paper Id: IJNRD2405129

Downloads: 000122255

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405129.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405129

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Call For Paper - Volume 10 | Issue 12 | December 2025

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