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Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?

Melanie Mathys, Marco Willi, Michael Graber, Raphael Meier

TL;DR

This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic images and critically assess the accessibility, practicality, and output quality of these tools and their implications in threat scenarios of deception, influence, and subversion.

Abstract

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.

Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?

TL;DR

This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic images and critically assess the accessibility, practicality, and output quality of these tools and their implications in threat scenarios of deception, influence, and subversion.

Abstract

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.
Paper Structure (37 sections, 56 figures, 1 table)

This paper contains 37 sections, 56 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Overview of Applied Methods
  4. Tools and Resources
  5. Cyber Influence Threat Scenarios
  6. Threat Scenarios
  7. Synthetic Force Multiplier
  8. Tailored Military Disinformation
  9. Sinaloa Cartel
  10. Environmentalists
  11. Undermining Democracy
  12. Potential of Generative Models
  13. Out-of-the-box Capabilities
  14. Illustrations and Cartoons
  15. Emotional expressiveness in human faces
  16. ...and 22 more sections

Figures (56)

  • Figure 1: Synthetic images generated with Stable Diffusion XL. Images adapted from podell_sdxl_2023 , licensed under https://creativecommons.org/licenses/by/4.0/.
  • Figure 2: Synthetic images generated with ControlNet conditioning. The first image on the left depicts a photograph https://unsplash.com/de/fotos/braunhirsche-tagsuber-auf-grunem-gras-aJuv14zf-ZY, by Y S, @santonii, on Unsplash noauthor_unsplash_nodate). The second image is the conditioning input (a canny edge map) derived from the photograph. The other images are synthetic and closely follow the canny edge map conditioning and (optionally) additional text input (the fourth and fifth images, see text below the images) which allows for more control over the generated images.
  • Figure 3: Synthetic images generated with Dreambooth. The images on the left are the training images / photographs depicting the subject of interest. The other images are synthetic and depict the target subject in different situations. Figure from ruiz_dreambooth_2022, licensed under https://creativecommons.org/licenses/by/4.0/.
  • Figure 4: Real image of a HIMARS vehicle. Photo credit: Cpl. Donovan Massieperez https://en.m.wikipedia.org/wiki/File:3dMarineDivisionHIMARS.jpg, licensed under https://creativecommons.org/licenses/by-sa/4.0/deed.en.
  • Figure 5: Synthetic images of HIMARS vehicles. Both images were created with ControlNet conditioning on canny edge maps from real photographs. Image \ref{['capt:sc1_best_gen_example1']} was created using the prompt: "photography of a HIMARS vehicle driving on a gravel road, nice weather, blue sky". Image \ref{['capt:sc1_best_gen_example2']} was synthesized with "a photography of a HIMARS in a military bunker, large space, clean structures, low light".
  • ...and 51 more figures