# Synthetic Face Discrimination via Learned Image Compression
## 2 Related Works  
Several techniques have been suggested in recent years to differentiate synthetic faces from real ones. Initially, several methods took advantage of various characteristics of the images, which were influenced by GAN models. [PERSON] et al. [12] used irregularities in the positions of facial landmarks like eye corners, the nose tip, and the mouth to identify fake images. Specifically, they utilized the fact that GANs are able to generate characteristics of the face such as the eyes, mouth, nose, etc., that individually are highly realistic, but that are positioned improperly on the face. On the other hand, [PERSON] et al. [13] utilized certain facial features such as the contour of the face and the color of the iris, since these are the features in which external manipulation is most easily distinguishable. They proposed a pipeline of features to be checked for the detection of synthetic faces through image  
Figure 1: Natural and synthetic images of human faces. (**a**) Natural faces from the CelebA_HQ dataset, (**b**) synthetic faces from the StyleGAN2 dataset, and (**c**) synthetic faces produced with stable diffusion. segmentation. Differences in eye color and iris size were the first to be looked at. In the next stage, they examined possible missing areas in the teeth, eyes, and reflections. Finally, they checked for irregularities at the nose tip and the face contour of the images. [PERSON] et al. [14] used both co-occurrence matrices and convolutional neural networks (CNNs) for their research. They extracted co-occurrence matrices from the three color channels of an image in the pixel domain and trained a model using a deep CNN architecture. It should be noted that the image was used as a whole instead of being split into its three color channels. [PERSON]'s [15] approach was along the same line, but also worked across color bands. In the spirit of exploiting the inconsistencies in color of the synthetic images, they utilized not only the spatial co-occurrence matrices--like Nataraj--but also the cross-band co-occurrences. These were then fed to a CNN too. Other methods have also utilized aspects of the colors in GAN-generated images. [PERSON] and [PERSON] [16] took advantage of the limitation of GANs in producing only certain pixel values and in avoiding the creation of regions with low exposure or high saturation by establishing two measurements that examine the correlation between color channels and saturation. These differentiations in intensity and exposure are caused by the normalization that is applied by GAN generators, which does not happen in natural images. Finally, [PERSON] et al. [17] based their method on the premise that GAN-generated images differ from natural ones in the chrominance components of the HSV and YCbCr color spaces, especially in the residual domain. Consequently, they proposed a set of features for the identification of synthetic images consisting of the co-occurrence matrices derived from the residual images of multiple chroma components. Other researchers noticed certain abnormalities that synthetic images display in the frequency domain. [PERSON] et al. [18] identified fake images by detecting the spectral peaks that show up because of the upsampling that takes place in many GAN architectures. This upsampling causes a \"checkerboard artifact\" in the spatial domain, which translates into replications of spectra in the frequency domain. This problem can be solved through a lowpass filter, but if too many frequency coefficients are removed, the GAN-generated image can become blurry, making its classification easier. Similarly, [PERSON] et al. [19] also utilized the artifacts caused by upsampling in the frequency domain. However, instead of the Discrete Fourier Transform (DFT), they used the Discrete Cosine Transform (DCT). In the DCT frequency domain, low frequencies contribute the most to a natural image. Because of this, big parts of the image can be approximated by using low-frequency functions.