Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916164230184960 |
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| author | Mathys, Melanie Willi, Marco Graber, Michael Meier, Raphael |
| author_facet | Mathys, Melanie Willi, Marco Graber, Michael Meier, Raphael |
| contents | The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic images. We critically assess the accessibility, practicality, and output quality of these tools and their implications in threat scenarios of deception, influence, and subversion. Notably, the report generates content for several hypothetical cyber influence operations to demonstrate the current capabilities and limitations of these AI-driven methods for threat actors. While generative models excel at producing illustrations and non-realistic imagery, creating convincing photo-realistic content remains a significant challenge, limited by computational resources and the necessity for human-guided refinement. Our exploration underscores the delicate balance between technological advancement and its potential for misuse, prompting recommendations for ongoing research, defense mechanisms, multi-disciplinary collaboration, and policy development. These recommendations aim to leverage AI's potential for positive impact while safeguarding against its risks to the integrity of information, especially in the context of cyber influence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12207 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat? Mathys, Melanie Willi, Marco Graber, Michael Meier, Raphael Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition K.4.0; I.2.0; I.4.0 The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic images. We critically assess the accessibility, practicality, and output quality of these tools and their implications in threat scenarios of deception, influence, and subversion. Notably, the report generates content for several hypothetical cyber influence operations to demonstrate the current capabilities and limitations of these AI-driven methods for threat actors. While generative models excel at producing illustrations and non-realistic imagery, creating convincing photo-realistic content remains a significant challenge, limited by computational resources and the necessity for human-guided refinement. Our exploration underscores the delicate balance between technological advancement and its potential for misuse, prompting recommendations for ongoing research, defense mechanisms, multi-disciplinary collaboration, and policy development. These recommendations aim to leverage AI's potential for positive impact while safeguarding against its risks to the integrity of information, especially in the context of cyber influence. |
| title | Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat? |
| topic | Computers and Society Artificial Intelligence Computer Vision and Pattern Recognition K.4.0; I.2.0; I.4.0 |
| url | https://arxiv.org/abs/2403.12207 |