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Diffusion Models and their Impact on Cybersecurity
Dvorščák, Patrik ; Homoliak, Ivan (oponent) ; Lapšanský, Tomáš (vedoucí práce)
This thesis explores the performance of diffusion models (DMs) and generative adversarial networks (GANs) in creating AI-generated visual content across multiple applications, including face synthesis, text-to-image generation, artistic rendering, image-to-image translation, video synthesis, and super-resolution. Through comparative experiments, this research evaluates the models' ability to generate detailed, realistic, and artistically compelling visuals from textual and image prompts. The results reveal that DMs excel in producing highly detailed images that closely follow text prompts, particularly effective in face synthesis and text-to-image tasks. In contrast, GANs are more adept at rendering realistic environmental scenes, suitable for applications requiring immersive visuals. Both model types are competent in artistic rendering, though they differ in style adaptation and creativity. The thesis concludes with future research directions aimed at enhancing model efficacy and integrating these technologies more effectively into practical applications.
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Determination of Objects Similarity Based on Image Information
Rajnoha, Martin ; Kamencay,, Patrik (oponent) ; Beneš, Radek (oponent) ; Burget, Radim (vedoucí práce)
Monitoring of public areas and their automatic real-time processing became increasingly significant due to the changing security situation in the world. However, the problem is an analysis of low-quality records, where even the state-of-the-art methods fail in some cases. This work investigates an important area of image similarity – biometric identification based on face image. The work deals primarily with the face super-resolution from a sequence of low-resolution images and it compares this approach to the single-frame methods, that are still considered as the most accurate. A new dataset was created for this purpose, which is directly designed for the multi-frame face super-resolution methods from the low-resolution input sequence, and it is of comparable size with the leading world datasets. The results were evaluated by both a survey of human perception and defined objective metrics. A hypothesis that multi-frame methods achieve better results than single-frame methods was proved by a comparison of both methods. Architectures, source code and the dataset were released. That caused a creation of the basis for future research in this field.
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Determination of Objects Similarity Based on Image Information
Rajnoha, Martin ; Kamencay,, Patrik (oponent) ; Beneš, Radek (oponent) ; Burget, Radim (vedoucí práce)
Monitoring of public areas and their automatic real-time processing became increasingly significant due to the changing security situation in the world. However, the problem is an analysis of low-quality records, where even the state-of-the-art methods fail in some cases. This work investigates an important area of image similarity – biometric identification based on face image. The work deals primarily with the face super-resolution from a sequence of low-resolution images and it compares this approach to the single-frame methods, that are still considered as the most accurate. A new dataset was created for this purpose, which is directly designed for the multi-frame face super-resolution methods from the low-resolution input sequence, and it is of comparable size with the leading world datasets. The results were evaluated by both a survey of human perception and defined objective metrics. A hypothesis that multi-frame methods achieve better results than single-frame methods was proved by a comparison of both methods. Architectures, source code and the dataset were released. That caused a creation of the basis for future research in this field.
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