# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
###### Abstract  
Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition.  
copy-move; A-KAZE; density clustering algorithm; affine matrix transformation; location similarity +
Footnote †: journal: Medical Image Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering  
**Citation:** [PERSON].  
Many researchers have explored image copy-move forgery detection in detail. Currently, there are two main types of methods to detect copy-move forgery: divide the image into image blocks or extract feature points from the image [5]. It was found that the block-based method, which requires matching a large number of overlapping blocks, is inefficient when it comes to geometric transformation attacks because block features are very sensitive to scale and rotation transformations, which ultimately results in high computational cost. Feature point-based methods are commonly used in copy-move forgery detection because they are unaffected by rotation and scaling, making them highly reliable.  
This paper proposes a forgery detection method based on two detection approaches, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to reduce the computational cost and extract more feature points in smooth regions. The SURF detector has a lower-dimensional feature descriptor and is invariant to rotation and scaling, resulting in faster speed. On the other hand, A-KAZE can extract a large number of useful feature points in smooth regions, allowing for improved accuracy of forgery detection in these regions. After feature matching with g2 NN for the two detectors, we adopted the DBSCAN algorithm to eliminate mismatched pairs and locate tampered areas by comparing similarities between the original image and the image transformed by the affine matrix. The experimental results demonstrate that the method proposed in this paper improved the accuracy of smooth region forgery detection by combining the two detection approaches and exhibited strong robustness to geometric transformations such as rotation and scaling.  
The rest of this article is arranged as follows: Section 2 provides an overview of the latest research on image copy-move forgery. Section 3 describes SURF and A-KAZE in detail. In Section 4, the proposed image copy-move forgery detection method based on fused features and density clustering is described in detail. Section 5 analyzes the performance of the proposed method and compares it with other algorithms through experiments. Finally, Section 6 summarizes the method proposed in this article.