# 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
The technique uses four directional (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\)) run-length run (RLRN) produced from gray-level run-length pixel number matrices of the de-correlated channel as distinctive features to isolate altered images. A novel framework suggested by [PERSON] et al. [45] uses a photometric uniformity of light in the shadows. Here, the color features of shadows are evaluated by the shadow-matte value. A picture extracts different matte values from shadow boundaries and the penumbra region. Then, consistency is compared. However, this technique works well only on images that have shadows. Improvising over CFA, [PERSON] et al. [46] proposed a method by calculating the existence of de-mosaicking artifacts left after tampering and then using a statistical model that calculates the geometric mean of the variance at the local level to arrive at the tampering probability of each of two \(\times\) two image block. However, this method works only over images taken by a digital camera where de-mosaicking algorithms are used. [PERSON] et al. [47] proposed combining DCT and DWT features. A Markov-based approach was taken where the Markov features were extracted from transition probability matrices in DCT, and additional elements were added from the DWT domain. For feature reduction, the SVM-RFE method is employed. Lastly, the SVM classifier is used. [PERSON] and [PERSON] [48] proposed using an illumination signature to detect images splicing over human faces. The signature is extracted from the face region existing in an image using the \"dichromatic reflection model\" (DRM). Illumination signature is the \"dichromatic plane histogram\" (DPH), calculated from the facial region present in an image by applying a 2D Hough Transform. However, this technique is specifically for images that have human faces. [PERSON] et al. [49] employ PCA over SVD in their implemented model, which uses the SVD-based feature extraction method to extract DCT features from an image. To reduce the feature dimensionality, the author applied Kernel PCA. The study was performed over Columbia datasets with different feature vectors. They found that the best accuracy was observed with 50 dimensions. [PERSON] et al. proposed a unique method using discrete octonion cosine transform (DOCT) and Markov [50]. The algorithm would first convert the image into the DOCT domain, and then the inter-block and intra-block Markov features are extracted in the DOCT area. LIBSVM is used as a classifier using Markov features. This method gave excellent accuracy results over CASIA ver1 and ver2 datasets but failed when the image size was too small. [PERSON] and [PERSON] [51] recently used machine learning logistic regression to identify the image splicing images. The proposed method first converts all images to Grayscale. In the feature extraction stage, it learns four different feature sets: LBP, Laws Texture Energy (LTE), HoG, and DWT (Wavelet Features). As stated above, these feature sets have been used individually in various research works. A logistic regression model is trained and used as a classifier, combining all 142 feature vectors extracted from these feature sets. The model proves its efficiency by giving more than 98% accuracy of CASIA 1.0, CASIA 2.0, and Columbia data sets. But when applied to photographs that have been severely downscaled, texture and clarity are destroyed. [PERSON] et al. [52] proposed a further novel concept by investigating the correlation of image noise over the RGB color channels over a spliced picture. [PERSON] et al. [53] employed a different conventional DCT method to find both modifications. [PERSON] and [PERSON] [54] proposed a framework depending on illumination-reflectance and LBP. The image is transformed into Y and CrCb color space using this technique. The illumination element is then derived using the illumination-reflectance approach. The LBP histogram is produced by illumination in the last stage, and CbCr is used as a function vector for classification.