# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 2 Related Works  
At present, copy-move forgery detection (CMFD) is divided into image block-based and feature point-based methods. Using the method of image blocks, the image is typically segmented into overlapping rectangular or circular blocks, from which feature points are  
Figure 1: Image copy-move forgery examples: (**a**) original images; (**b**) forged images; (**c**) binarized truth images. extracted, and then feature matching is conducted. [PERSON] et al. [6] first proposed a block-based method that uses CMFD with discrete cosine transform (DCT) in this algorithm. Several features were proposed to describe these blocks. For example, [PERSON] et al. [7] proposed a method based on principal component analysis (PCA) for image CMFD. This method extracts image features using the Haar transform, simplifies the features through PCA to reduce computational complexity, and then detects, locates, and removes false boundaries. Additionally, the texture features of the input image are analyzed using the gray-level co-occurrence matrix (GLCM). [PERSON] et al. [8] applied a two-dimensional discrete wavelet transform (DWT) to tampered images, considering only the approximate DWT coefficients for block segmentation, and matching was carried out by computing the distance between pairs of blocks. The results showed that the algorithm was not suitable for forgery detection under geometric transformation attack. Finally, the features were matched using Euclidean distance, and non-matching feature points were identified. [PERSON] et al. [9] proposed a method for image CMFD that utilizes local binary patterns (LBP) and Harlick features for feature extraction. In order to verify the authenticity of the image, various supervised machine learning classifiers, such as support vector machines (SVMs), random forest (RF), and gradient boosting classifiers, were used, and the forgery detection performance based on these classifiers was analyzed. In order to address the problem of tampering operations such as rotation and scaling in the tampered area, many local invariant features have been applied in the research of CMFD forensics, such as Zernike moments, Hu moments, and so on [10; 11]. [PERSON] et al. utilized statistical features based on a histogram of gradients (HOG) for CMFD [12]. As can be seen from the results, the algorithm's performance in detecting forgeries in the case of rotation and scaling of large regions needs improvement. A perceptual image hashing scheme based on block truncated coding is presented in [13]. In this paper, centrosymmetric local binary patterns are used as image feature descriptors. This method requires a small amount of calculation and is not sensitive to grayscale changes. [PERSON] et al. [14] proposed a CMFD algorithm based on the polar complex exponential transform (PCET). In this algorithm, the suspicious image is first divided into overlapping circular blocks, and PCET is used to extract the geometric invariant features of each block. However, dividing the image into circular blocks results in some loss of image information, which may have a certain impact on the detection performance of the algorithm. Due to the high computational complexity of block-based methods and their limitations in detecting scale displacement tampering, researchers have shown increasing interest in feature point-based methods for image CMFD to address these problems. These methods first extract feature points from high-entropy regions of the image, then calculate and match feature vectors corresponding to each point, and finally identify tampered regions. The advantage of these methods lies in the point extraction, point description, point matching, and positioning methods applied [15; 16; 17; 18; 19; 20]. In order to enhance the sensitivity of copy-move detection algorithms to manipulations such as rotation and scaling, [PERSON] et al. [21] proposed an early feature point-based CMFD method.