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Use of Diffusion Models in Deepfakes
Trúchly, Dominik ; Malinka, Kamil (oponent) ; Lapšanský, Tomáš (vedoucí práce)
A deepfake is a type of synthetic media created through sophisticated machine learning algorithms, particularly deep neural networks. As an example Generative adversarial neural networks (GANs), that are capable of generating images that are almost impossible for ordinary individuals to differentiate from genuine reality. Consequently, deepfake detection algorithms have been developed to address this growing concern. Leveraging advanced machine learning techniques, these algorithms analyze various features within images and videos to identify inconsistencies or anomalies indicative of manipulation. This thesis investigates the application of diffusion models, commonly utilized in digital image processing to enhance image quality by reducing noise and blurring, in bolstering the realism of deepfakes. By using these models, we test their effect on detecting deepfakes images using deepfake detectors.

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