Generation and Generalized Detection of Fully Synthetic Photorealistic Images
Abstract
Image generation has reached a point where a machine learning model can create an image that looks like it could have been captured with a camera. People using these models can limitedly control the creation process, quickly synthesizing images from text. Although the controlled synthesis of images opens up a great number of creative possibilities, it can also be used for nefarious purposes, such as identity theft. Being able to detect if an image is synthetic could help in mitigating possible harm. This thesis studies the methods used for both creating and detecting synthetic images by performing a literature review for both topics. The main goals are to find the key ideas, recent advancements, and challenges behind synthetic image generators and detectors, and test how generalizable current open-source pretrained detectors are. Generative adversarial networks, autoregressive models, and diffusion models were recognized as the most popular generation methods. Although their implementations differ, they face similar challenges, such as the need for large datasets, which produce biases and ethical concerns regarding the use of the generators. For detection, multiple methods have been suggested, with many distinguishing patterns both in synthetic images and those captured with digital cameras. Watermarking was also identified as a detection solution, but it is difficult to implement universally. Experiments were conducted on open-source detectors, where synthetic and real…
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