# Synthetic Face Discrimination via Learned Image Compression
## 1 Introduction  
Generative artificial intelligence (AI) models are rapidly expanding and have already been integrated into various applications [1, 2]. Progress has been influenced in part by the development of novel methods like Generative Adversarial Networks (GANs) [3, 4] and, more recently, Diffusion Models [5, 6], enabling the generation of synthetic data with exceptional realism and quality, achieving unmatched levels of photorealism. Simultaneously, there is potential for malicious users to employ generative models to spread misinformation across multiple social platforms. Additionally, there is a growing issue with verifying the authenticity of images, as this becomes progressively more difficult [7]. Figure 1 depicts the similarity between real and synthetic face images. It is clear that both GANs and Diffusion Models produce images that are often indistinguishable to the naked eye. However, both image-generation methods have some issues. GANs have certain limitations, such as mode collapse, in which the generator fails to replicate the entire distribution of the training data, which results in repetitive or limited outputs. Due to the adversarial nature of the training process between the generator and the discriminator, the robustness of the discriminator is of utmost importance for the generation of realistic outputs. Additionally, these networks have difficulty replicating the high-level semantic features of real images. The most obvious visual artifacts that we encounter in GAN-generated face images usually involve inconsistencies in the color or symmetry of specific facial characteristics, i.e., the eyes [8]. On the other hand, Diffusion Models transform noise into an image through an iterative diffusion process. Since they are essentially graphics, they often display a lack of 3D modelling for objects and surfaces. As a result, there are often asymmetries in features such as shadows and reflections. Many images generated with Diffusion Models also exhibit a general inconsistency in brightness [9, 10]. Finally, both image generators leave some traces that can only be found through statistical analysis of the image, both in the spatial and in the frequency domain [11]. The distinction between artificial and natural images has garnered significant interest among researchers in multimedia forensics. The most common method for identifyingsynthetic images typically requires training a neural network for binary classification (natural versus artificial) using a vast dataset containing labeled images. A crucial aspect of this procedure often involves the application of a carefully selected range of image enhancements in the training stage [1]. In this work, we develop a new synthetic face discrimination method that is not based on semantically meaningful features of an image. Our approach follows a completely different concept. Specifically, we study the response of real and fake face images to deep learning-based compression, and we distinguish them based on the differences of their quality after compression. Our goal is to develop an alternative technique to GAN-based detection methods that is also computationally efficient. Additionally, we aim at rendering it to be more generalized than many GAN-based methods that excel only when dealing with images generated by GANs, thus successfully also classifying images produced by Diffusion Models. The technique proposed in the present work offers a new way to address the issue of DeepFake detection by using learned compression to distinguish synthetic face images, a method not previously attempted. This differentiates the proposed approach from similar studies that utilize deep learning methods for the same purpose. It examines the classification problem from an alternative perspective. It can be broken down into two separate techniques which both work well independently: compression and binary classification. Furthermore, it is capable of identifying images produced by diverse models, such as GANs and Diffusion Models.