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2000
Volume 15, Issue 2
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Aims and Background

Digital image forgery has emerged as a significant threat in an era where visual content plays a crucial role in communication and authentication. The rise of sophisticated manipulation techniques demands innovative approaches for reliable detection.

Objectives and Methods

This research introduces a novel methodology for Digital Image Forgery Detection using Noise Cancellation in Feature-Map Convolutional Neural Networks (NC-FM-CNN). Our approach focuses on exploiting the inherent patterns of manipulated images by integrating a noise cancellation mechanism within the CNN architecture. The use of feature maps enables the network to discern subtle alterations in image content, offering enhanced sensitivity to forged regions. By selectively filtering out noise patterns introduced during the forgery process, the model can more accurately pinpoint areas of manipulation. The proposed NC-FM-CNN architecture undergoes extensive training on diverse datasets encompassing various types of image manipulations, ensuring its adaptability to a wide range of forgery techniques. The network's ability to learn and differentiate between authentic and manipulated features is enhanced through advanced optimization techniques and regularization methods.

Results

Our experimental results, showcasing an accuracy of 97%, demonstrate the superior performance of the NC-FM-CNN compared to traditional forgery detection methods. The model exhibits robustness in detecting forged content even in cases where manipulations are subtle or deeply embedded. Moreover, its efficiency in handling diverse forgery scenarios positions it as a versatile tool for forensic analysis in digital image authenticity verification.

Conclusion

As image manipulation techniques continue to evolve, the proposed NC-FM-CNN framework offers a proactive and reliable solution for combating digital forgery, contributing to the establishment of a trustworthy digital ecosystem.

© 2025 Bentham Science Publishers
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2024-08-15
2025-09-13

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Unmasking Deception Harnessing Noise Cancellation for Digital Image Forgery Detection Using Feature-Map Convolutional Neural Networks
Int J Sensors Wirel Commun Control 15, 184 (2025); https://doi.org/10.2174/0122103279316771240725112305
/content/journals/swcc/10.2174/0122103279316771240725112305
/content/journals/swcc/10.2174/0122103279316771240725112305
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  • Article Type:     Research Article
Keyword(s): deep learning; error level analysis (ELA); FM-CNN; Image forgery detection; image manipulation; noise cancellation

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