Affiliations: [a] Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| [b] Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
| [c] Department of Software Engineering, University of Engineering and Technology, Taxila, Pakistan
| [d] College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| [e] Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
| [f] Artificial Intelligence & Data Analytics (AIDA) Lab, CCIS, Prince Sultan University, Riyadh, Saudi Arabia
Correspondence:
[*]
Corresponding author. Zahid Mehmood, Department of Computer Engineering, University of Engineering and
Technology, Taxila 47050, Pakistan. E-mail: zahid.mehmood@uettaxila.edu.pk.
Abstract: ‘With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.
