# 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
#### 4.1.2 Image Splicing  
Detecting image splicing is relatively more challenging than copy-and-move tampering. There is comparable lineation of the object of the same image as copy-and-move tampering can have equal transitions, texture, length, and many others, while in image splicing, different former segments are introduced with different textures and image characteristics complexity. [PERSON] and [PERSON] [40] proposed a method using bicoherence values. The feature values computed from the bicoherence of a spliced image's horizontal and vertical 1-D slices are a detection technique. They observed that image splicing increases the value of the bicoherence magnitude and phase features. The detection accuracy of this model is about 70%. The method does not work well when other non-splicing factors manipulate the image. The alternative method proposed by [PERSON] and [PERSON] [41] used various CFA interpolations in digital cameras. The correlation of the CFA interpolation is disturbed when tampering is performed. The variance is calculated between the blocks. This method is limited to pictures from digital cameras, which use CFA. After a detailed evaluation study of image splicing, [PERSON] et al. suggested methods founded on Hilbert-Huang transform in 2006, and the next year [42] improved it with wavelet characteristic functions along with 2-D phase congruency statistics. They observed that splicing leaves traces of image tampering, specifically at locations with sharp image \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S. No** & **Author** & **Detection Techniques** & **Dataset** & **Results** & **Pros** & **Cons** \\ \hline
1 & [PERSON] et al. [24] & DWT + SVD & Self-created Image set & Not provided & Can handle JPEG compression & Fails after 70 factors \\ \hline
2 & [PERSON] et al. [26] & SVD & 200 Images & 86\% & Can handle scaling, rotation, and blurring & JPEG compression and contrast/brightness have low results \\ \hline
3 & [PERSON] et al. [28] & Local Binary Pattern & 200 images from the Internet & Correct detection & Can handle rotation, blurring, blurring, compression & Images rotated at general angles \\ \hline
4 & [PERSON] et al. [29] & Histogram of Oriented Oriented Gradients & CoMoFod & FC factor \(>90\%\) & Can handle small rotation, blurring, and contrast & High scaling and high rotation \\ \hline
5 & [PERSON] et al. [30] & Multiscale Weber’s Law & CASIA 1.0 & Accuracy & Can handle rotation, compression, noise & Cannot tell localization of tampered image \\  & & Descriptor MWLD & Columbia & 94.17 & & \\ \hline
6 & [PERSON] and [PERSON] [16] & DWT + SURF & 50 Images from MICC-F2000 & 95\% Accuracy & Good with geometric transformation & Not verified with compression and AWGN noise \\ \hline
7 & [PERSON] et al. [32] & FrQZMs & FAU and GRIP & F-Measure of 0.9533 over GRIP and 0.9392 over FAU & Can handle scaling and noise processing & Low results with rotation angles and JPEG compression \\ \hline
8 & [PERSON] et al. [35] & Robust Hashing & UADFV, CycleGan, StarGan & 0.83, 0.97, and 0.99 F-score & Can handle noise, compression, and resizing & Not verified on social media images \\ \hline
9 & [PERSON], [PERSON], [PERSON], [39] & SIFT with HSF algorithm & CMH and GRIP & F-Score 91.50 on CMH. 92.11 & Work well on geometrical attacks and post-processing disturbances & Not verified on social media images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of techniques used in copy–move detection using forensic features. transitions. [PERSON] et al. [43] suggested a method based on a gray-level co-occurrence matrix (GLCM). Here, the feature is extracted based on image edges in the chroma channels. Strong signal denoting the image content was ignored, while the low signals, i.e., the edges of spliced images, were preserved. LIBSVM is used as a classifier. In an improvisation to [PERSON] et al., [PERSON] et al. [44] suggested using chroma image spaces.