# 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  
For cloning detection, two main approaches exist: feature-based and hash-based. Many of the detection algorithms are developed based on features. In contrast, the hash-based method is used only in the case of plain cloning detection, i.e., when the copied image fragment is not transformed/processed. The key gain of hash-based algorithms is to use a low computational complexity. [PERSON] et al. [21] shared a review of various copy-and-move studies but covered only some of the techniques as it was an evaluation comparison paper. Initially, [PERSON] et al. [22] implemented a copy-move tampering detection algorithm using quantized Discrete Cosine Transformation coefficients (DCTs) on small overlying blocks. An image is scanned by BxB block size, and its feature vectors are calculated using DCT. Then, a block comparison is examined after feature vectors are lexicographically sorted. Observing irregular patterns is formed by blocks that match copy-move tampering. [PERSON] and [PERSON] [23] improvised the DCT-based overlapping block algorithm using principal component analysis (PCA). The small block sizes were passed through PCA to reduce the features. Using PCA, the authors could reduce the features to almost half the feature numbers by [PERSON] et al. [22]. The technique is effective and can handle a little noise, but it fails to detect a copy-move image having rotations efficiently. Another method proposed by [PERSON] et al. [24] used a Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based on a sorted neighborhood approach. The picture is reduced in dimension using the DWT method, and then the SVD is applied over components having low frequency for obtaining the feature vectors. The technique works well even if JPEG is compressed to 70-quality levels but fails if more compression is applied. [PERSON] et al. [25] applied Fourier Mellin Transform (FMT) properties to detect cloning invariant to scaling, rotation, and translation. The author proposed counting bloom filters rather than lexicographic sorting to improve computational efficiency. It was observed that there is a linear correlation between pixels in the real image to handle rotations, which becomes distorted upon applying any tampering technique [26]. They employed SVD for feature detection extracted from image sub-blocks. This method could detect tampering, such as geometric transformation and brightness alteration. The technique was found robust against rotation. An enhanced DCT-based technique was employed by [PERSON] et al. [27] by including a truncating process that would ignore the blocks having higher frequency coefficients. This process was used to decrease the dimension of the feature vector for fake detection. The results were quite robust to AWGN distortions besides JPEG compression. In another method, instead of square blocks, circular blocks were used [28]. The picture was divided into overlying circular blocks. The feature extraction of the blocks is attained through consistent Local Binary Patterns (LBP), which are invariant to rotation. The method was robust to different transformations like compression, rotation, blurring, flipping, and AWGN. However, the technique fails to spot tampered regions rotated with random angles. Another novel technique proposed by [PERSON] et al. [29] suggested using the Histogram of Oriented Gradients (HOG) for feature detection over overlapping blocks. The HOG features would detect the forged regions. However, the technique required improvements where tampering was performed over large areas in an image. At the same time, [PERSON] et al. [30] suggested using a multiscale Weber's law descriptor (WLD) histogram for feature detection. The multi-WLD abstracts feature from chrominance components of the picture. The method used SVM as a classifier and was evaluated over datasets like CASIA 1.0, CASIA 2.0, and Columbia. Another technique used the DCT technique with Gaussian RBF kernel PCA [31] to reduce feature vectors.