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Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics

Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics

Balasaheb H. Patil
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 21
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781683181538|DOI: 10.4018/IJSIR.304401
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MLA

Patil, Balasaheb H. "Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics." IJSIR vol.13, no.3 2022: pp.1-21. http://doi.org/10.4018/IJSIR.304401

APA

Patil, B. H. (2022). Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics. International Journal of Swarm Intelligence Research (IJSIR), 13(3), 1-21. http://doi.org/10.4018/IJSIR.304401

Chicago

Patil, Balasaheb H. "Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics," International Journal of Swarm Intelligence Research (IJSIR) 13, no.3: 1-21. http://doi.org/10.4018/IJSIR.304401

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Abstract

This paper intends to propose a new model for detecting the patch based inpainting operation using Enhanced Deep Belief Network (E-DBN). The proposing model makes strong supervising of DBN that will capture the manipulated information. In fact, the enhancement is done under optimization concept, where the activation function and weight of DBN is optimally tuned by a new hybrid algorithm termed as Swarm Mutated Lion Algorithm (SM-LA). The hybridization model combines two conventional models: Group Search Optimizer (GSO) and Lion Algorithm (LA). Finally, the performance of proposed model is compared over other conventional models with respect to certain performance measures.

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