Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy

International Journal of Image, Graphics and Signal Processing 2024
Prabhu Bevinamarad, Prakash Unki, Padmaraj Nidagundi

Due to the availability of low-cost electronic devices and advanced image editing tools, changing the semantic meaning of a particular image has become straightforward by employing various image manipulation techniques like image copy-move, image splicing and removal operations. The tampered images with this sophisticated software are rich in visualization, making the modifications invisible to the naked eye. Detecting these image alterations is laborious, time-consuming, and often yields inappropriate results. The current techniques use conventional square, slide regular, and artifacts procedures to identify image deviations to combat image forgery practices. Still, these techniques exhibit problems related to generalization, training and testing, and model complexity. So, in this paper, a novel image forgery detection and localization framework is implemented using stationary wavelet transform (SWT), and a Hybrid Dilated Adaptive VGG16 model with optimization is introduced to classify forgery images and localize the forgery regions present in an image. Initially, the proposed framework processes the input image with SWT to decompose an image into different subband and further divide it into patches. After that, the hybrid dilated adaptive VGG16 Network (HDA-VGG16Net) is built to extract the deep image features from the patches. Later, the Hybridized Tuna Swarm with Bald Eagle Search Optimization (HTS-BESO) technique is applied to optimize the VGG16 parameters. Finally, feature matching is formed using multi-similarity searching to recognize whether the input image is forged or original by locating forgery regions. The evaluation results are compared with existing forgery detection approaches to ensure the efficiency of the developed model by considering multiple performance measures.

Keywords
Copy-move forgery | Stationary wavelet transform | Dilated CNN | Tuna swarm optimization | Bald eagle search optimization
DOI
10.5815/ijigsp.2024.01.04
Hyperlink
https://www.mecs-press.org/ijigsp/ijigsp-v16-n1/v16n1-4.html

Bevinamarad, P., Unki, P., Nidagundi, P. Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy. International Journal of Image, Graphics and Signal Processing, 2024, Vol. 16, No. 1, pp. 38-60. ISSN 2074-9074. e-ISSN 2074-9082. Available from: doi:10.5815/ijigsp.2024.01.04

Publication language
English (en)