A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Authors

  • Uliyan Diaa Department of Information Security, College of Computer Science and Engineering, University of Ha'il, Saudi Arabia
Volume: 14 | Issue: 1 | Pages: 12549-12555 | February 2024 | https://doi.org/10.48084/etasr.6622

Abstract

Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.

Keywords:

SURF key points, SLIC segmentation, image forgery, deep learning

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How to Cite

[1]
U. Diaa, “A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12549–12555, Feb. 2024.

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