# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.1 Copy-and-Move/Cloning
Overall, this method significantly reduced the feature-length without compromising the results. The results observed were as good as the multi-WLD approach. [PERSON] et al. [17] performed a comparative study of various methods like DWT, LBP, and Scale Invariant Feature Transformation (SIFT), and they found that the SIFT-based approach was better than others. At the same time, [PERSON] and [PERSON] [16] used a fusion of DWT and Speeded Up Robust Features (SURF). DWT is applied to reduce the photo's dimension, and SURF is used to extract the main points from the image. The technique has been verified with JPEG and BMP format images comprising the genuine and fabricated image set. In contrast, [PERSON] et al. [32] proposed a novel method where fractional Zernike moments (FrZMs) are summed to fractional quaternion Zernike moments (FrQZMs) using quaternion algebra. The algorithm considers the FrQZMs as features and uses an enhanced PathMatch algorithm to match the elements. The algorithm worked well on color images and was evaluated over publicly available datasets FAU and GRIP. [PERSON] and [PERSON] [33] utilized a \"Center Surround Extrema\" (CenSurE) detector for detecting keypoints within the forged images. The \"Local Image Permutation Interval Descriptor\" (LIPID) was used to perform the keypoint feature computation. Keypoint feature coupling uses the \"k-nearest neighbor\" (k-NN) method. Conversely, [PERSON], [PERSON], and [PERSON] [34] used sophisticated template matching and speeded-up robust features (SURF) approach. The hashing algorithm was used by [PERSON], [PERSON], and [PERSON] [35] to identify picture modifications. This technique may also identify photos that have been compressed after being altered. [PERSON], [PERSON], and [PERSON] [35] refined the hashing method for better outcomes. [PERSON] et al. [36] offered yet another unique approach. [PERSON] and others used a method with two stages. The Grid-Based Filter and the Clustering-Based Filter were the two filters. [PERSON] et al. [37] employed a textual form of the input image extracted using the suggested approach. Since textual pictures are the source of the SIFT keypoints and descriptors, more robust keypoints and descriptors were used. Keypoint matching identifies suspicious areas and assesses whether the picture is fake. The Citratefi-based technique is used to localize the fabricated pixel. [PERSON] and [PERSON] [38] proposed a new CMF detection method that takes into account a few of the strongest KPs, selected from both FAST-corner KPs and \"Difference of Gaussian\" (DoG)-based KPs, evaluates SIFT descriptors, applied DWT for dimensionality reduction, and uses optimization based on football games (FGBO). The FGBO is a member of the meta-heuristic optimization algorithm family. [PERSON], [PERSON], and [PERSON] [39] have employed SIFT methods for copy-move detection. They used FLM and HSF algorithms to reduce computation and filter out outliers. Table 1 provides a computational efficiency comparison between the stated above methods. It illustrates that using a dimensionality reduction technique like PCA gives the same or better results with fewer features. Table 2, on the other hand, describes the pros and cons of various methods. It is clear that when images are subjected to any other alteration like compression or rotation, detecting copy-move manipulation becomes harder, and the technique fails to spot them as fake. Multiple manipulations are widespread among images shared over social media. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Feature Length** & **Feature** \\ \hline [PERSON] et al. [22] & 64 & DCT \\ \hline [PERSON] and [PERSON] [23] & 32 & PCA \\ \hline [PERSON] et al. [24] & 8 & DWT + SVD \\ \hline [PERSON], et al. [25] & 45 & FMT \\ \hline [PERSON] et al. [27] & 16 & Improved DCT \\ \hline [PERSON] et al. [31] & 10 & DCT and KPCA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computational efficiency comparison—copy-and-move.