An optimized CNN model for copy-move forgery detection and localization in digital images using particle swarm and Grey wolf optimization algorithms

Image data is increasing daily at a rapid speed, and at the same time, image forgery has become widespread with readily available and accessible software tools. The image manipulations can be performed in different ways. But, copy-move image forgery is one of the prominent types of malicious forgery...

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Veröffentlicht in:	Evolutionary intelligence Jg. 18; H. 6; S. 127
Hauptverfasser:	Bevinamarad, Prabhu, Unki, Prakash H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025 Springer Nature B.V
Schlagworte:	Algorithms Applications of Mathematics Artificial Intelligence Artificial neural networks Bioinformatics Control Datasets Deep learning Digital signatures Engineering Feature extraction Feature maps Forgery Heuristic methods Image compression Image manipulation Machine learning Mathematical and Computational Engineering Mechatronics Methods Optimization algorithms Parameter identification Particle swarm optimization Performance evaluation Research Paper Robotics Social networks Software Statistical Physics and Dynamical Systems Convolution neural network Digital forensics Particle swarm optimization Gray wolf optimization Copy-move forgery Hyperparameter tuning
ISSN:	1864-5909, 1864-5917
Online-Zugang:	Volltext
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