# 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
#### 4.1.5 GAN-Generated Images  
It is tough to identify GAN images using forensic methods. Though some reasonable attempts have been made using forensic methods, deep learning methods show better results. The current work in detecting GAN-generated forged images primarily emphasizes using signal-level features for spotting the faux. [PERSON] et al. [67] examined the GAN generators, and they observed that the frequency of saturated pixels is limited and that RGB channels are collapsed using weights that are unlike the spectral sensitivities of a digital camera. Based on the frequency of over-/under-exposed pixels, it uses a basic forensic to spot the distinction between GAN-generated and camera imagery. The work introduced intensity noise histograms for classifying authentic and GAN-generated images. As an alternative, [PERSON] et al. [68] suggested taking the color co-occurrence matrix as input. The matrix was extracted from the pixel domain's RGB channels for taking spatial correlation features. This feature set is then fed into the CNN framework. The framework was verified against CycleGan and StarGan datasets, with an accuracy of 99%.  
For deepfake detection, the \"Deepfake Detection Challenge\" (DFDC) was organized by the National Institute of Standards and Technology (NIST). Later, Facebook also launched a similar competition. [PERSON] et al. suggested that visual artifacts would be enough to detect deepfakes [69]. The proposed model uses differences in eye color to detect generated faces. Iris pixels of the eye region are used to calculate the color saturation variance. It also checks that the distance between the center of the iris and the center of the eye should be similar for both eyes. However, this method is limited to images that have a human face with bright, open eyes. For deepfake videos, [PERSON] et al. [70] proposed that unrealistic eye blinking can be used to detect face-swapping. The model used CNN-based VGG16 to learn this physiological signal of eye blinking using the Eye Aspect Ratio (EAR). The model becomes confused and gives inaccurate results when the eye region is small in the frames. It also needs to be improved for the dynamic pattern of blinking.  
The model used residual signals of chrominance components from multi-color spaces. These signals, including HSV, YCbCr, and lab, were passed through a shallow CNN model to learn the representation. In the end, a Random Forest was used as a classifier. The model was verified against images having compression, rotation, noise, and resizing. [PERSON] et al. proposed a deep learning method using the ELA for face swap detection [71]. The ELA technique uses the principle of having different ratios of image compression. The model suggests using images going through the ELA process before passing it to a CNN model. The CNN learns counterfeit feature vectors from ELA-processed images and identifies them as fake or real. The technique works well for face swap, with compression, but images without compressions. A \"pixel-region network\" (PRRNet) method to detect face forgery was proposed by [PERSON] et al. [72].