# Synthetic Face Discrimination via Learned Image Compression
## 2 Related Works
However, in GAN-generated images, this role is played by high frequencies, which cause visual artifacts such as grid patterns, etc. The researchers took advantage of these differences for the discrimination of deepfakes. On the other hand, [PERSON] et al. [20] proved that synthetic images do not have similar spectral distributions to real ones. Thus, they proposed a detection method that takes into account power and energy spectral distributions. Their research included a spectral regularization term that reduced the high-frequency distortions of GAN-generated images. Thereby, the final loss of the generative network was a weighted sum of the generator's loss and the spectral loss. A different solution to this problem was proposed by [PERSON] et al. [21]. A standard image recognition architecture model based on residual networks, ResNet50, was chosen and subsequently trained with augmented data in compression and blurring techniques. ResNet50 was selected due to its depth and utilization of skip connections, which tackle the issue of vanishing gradients in extremely deep networks. Additionally, the downsampling in the first layer was omitted for improved results, as was first suggested by [PERSON] et al. [22]. [PERSON] et al. [23] followed a different approach to this by using the semantic features of images. They attempted to differentiate between images generated by GANs and natural images by utilizing differences and dissimilarities in the eyes of synthetic faces that do not exist in natural faces. In order to solve this issue, they used a Siamese Neural Network (SNN) architecture to extract high-level features regarding the symmetry between the eyes; in real faces, the eyes share similar patterns, something which is not true for faces generated by GANs. [PERSON] et al. [24] exploited handcrafted features and,more specifically, the different textures and sensor noises exhibited by natural and synthetic face images generated using GANs. They sent the image through two pipelines: the first one examined the difference in texture through uniform local binary patterns (LBP) and the second one checked sensor noise through subtractive pixel adjacency matrices (SPAM). [PERSON] et al. [25] opted for a more generalized method. By replacing the residual network of [PERSON] [21] with a pre-trained vision language model (VLM), more specifically, Contrastive Language-Image Pre-Training (CLIP), they were able to detect images generated not only by GANs, but also Diffusion Models. Additionally, they performed training with limited samples--only a few pairs of real and synthetic images were given to a Support Vector Machine (SVM) classifier, unlike other methods. Finally, [PERSON] et al. [1] took it to the next level by developing a technique that was able to make generalizations across different concept classes, e.g., the model was trained on images of animals but tested on flower images. They measured the quality of the images, ranked them, and chose the images with the best quality score to be used for training. This way, the model focused less on the artifacts that were in an image and used features that were irrelevant to its content, leading to the aforementioned generalization. The shared characteristic of all these deep learning-based detection methods, as well as the more traditional approaches, is their reliance on exhaustive image analysis to extract specific features that help distinguish synthetic images. Image compression operates in a similar manner by analyzing and decomposing an image before reconstructing it. This similarity inspired us to explore whether compression could also be the basis for a detection technique. Moreover, since most images are already compressed in formats like JPEG, they have already undergone analysis and are readily available for use.