Image tampering detection in image forensics using earthworm‐rider optimization

Summary This article introduces a novel earthworm‐rider optimization algorithm (EW‐ROA), by integrating the earthworm algorithm in the rider optimization algorithm for the detection of the tampered regions optimally. Initially, the individual binary maps are generated using the image blocks of the i...

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Bibliographic Details
Published in:Concurrency and computation 2022-11, Vol.34 (26), p.n/a
Main Authors: Cristin, Rajan, Premnath, Sasi Padma, Ananth, John Patrick
Format: Article
Language:English
Subjects:
Algorithms
earthworm algorithm
Feature extraction
forensic tools
image forgery
Image manipulation
Neural networks
Optimization
rider optimization algorithm
tampering detection
Worms
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Summary:Summary This article introduces a novel earthworm‐rider optimization algorithm (EW‐ROA), by integrating the earthworm algorithm in the rider optimization algorithm for the detection of the tampered regions optimally. Initially, the individual binary maps are generated using the image blocks of the input image are fed to five forensic tools, and then they are concatenated into a single binary map. The features are extracted from the binary map moreover they are feed to classifier for the detection of the tampered image via the deep belief neural network, which is trained using the proposed earthworm‐rider optimization algorithm. The proposed system attains a highest accuracy of 0.9402, sensitivity of 0.98, and specificity of 0.98 that shows the superiority of the proposed system in effective tampering detection.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7293