Volume 18, No. 6, 2021

Robust Image Forgery Detection Methodology Based On Glow-Worm Optimization And Support Vector Machin

Bokefode Shudhodhan Balbhim , Harsh Mathur


The authentication of digital images is a significant challenge over the internet. First, the image editing software changes the original images into multiple images. Then, it is published over the internet—the printed images damage the identity and reputation of a person and things. Image forgery detection plays a vital role in detecting the original image and forged image over the internet. The incremental approach of various authors proposed algorithms and models for detecting forgery. This paper proposed a feature optimization-based image forgery detection method. The proposed method optimized the features using a glowworm optimization algorithm and support vector machine. The glowworm optimization algorithm optimized the lower content of features such as the texture of images and improved detection ratio. The proposed algorithm has been simulated in MATLAB tools and tested with reputed copy-move database comofod_samll. The evaluation results of the proposed algorithm suggest that the proposed algorithm is efficient instead of SVM and CNN algorithms.

Pages: 3697-3710

Keywords: image Forgery, Detection, Machine Learning, DWT, GSO

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