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Sprint projectNov 23, 2025Saarbrücken AISS

Raccognize - have AI companies stolen my images?

Leon Banik, Sandu Robert , Amelie Biewer, Alan Said Lopez Lopez · Team LARAcoon

Submitted to Defensive Acceleration Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

We built a little detective for images, with which we can see if an image is in the training data of a diffusion model (Like DALL-E, stable diffusion or midjourney). We do this by taking your image that you wanted to be checked, describe it (think auto alt text) and then feed it a number of times into the diffsuion model (in our code for test purposes SDXL), then we pick the best visual match. How do we determine the best visual match? Well theres been a lot of research papers on this topic that dont combine each others statstics, so we thougt we should fix that, and used all of them we thougt are relevant (9 in total). With the best image choen, we then trained an ML model on the weights for those statstics, so how significant each statistic is, and give you one final confience score that shows you how confident that AI is in the image being in the dataset the AI used to train. With only 200 images used in our training set for this model, we already got an accuracy of 77%, with much more to gain.

All of this fancy technology gets sent to a (very pretty) flutter frontend, that shows this data in nice chunks, and explains what the additional values mean. It also shows you the "most visually similar" image the AI generated. The data gets sent between the backend and frontend using fastAPI.

If you want to try it out, the APK in the release section of our github will work until Saturday Nov23 early mroning europe time, because then our credits for hosting the api and backend will run out.

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Does this reduce AI-related catastrophic or existential risks?

Scoring guide
  1. 1Minimal Impact. The project has minimal relevance to AI safety. It doesn't meaningfully address risks uniquely enabled or accelerated by advanced AI systems.
  2. 2Tangential Connection. The project touches on AI safety concepts but lacks depth or specificity. The connection to AI-enabled threats (bio, cyber, or AI misuse) is weak or unclear.
  3. 3Clear AI Safety Value. The project clearly reduces AI-related risks with valuable contributions. It addresses specific threats from AI systems and engages meaningfully with biosecurity, cybersecurity, or AI safety challenges.
  4. 4Significant Impact Potential. The above, plus the project demonstrates scalable safety mechanisms or defensive approaches. It shows clear potential to buy time for solving harder problems like alignment, or creates positive externalities for the broader AI safety ecosystem.
  5. 5Major Advancement. The above, plus the project represents a significant leap forward in defensive AI safety. Judges would eagerly share this with biosecurity, cybersecurity, or AI safety researchers and expect it to influence the field.

Does this strengthen the shield against AI-enabled threats?

Scoring guide
  1. 1Minimal Relevance. The project is only tangentially related to defensive technology or societal protection. Connection to biosecurity, cybersecurity, or defensive infrastructure is unclear or missing.
  2. 2Some Relevance. The project has some relevance to defensive acceleration, but the connection is broad or generic. It touches on defense without specific focus on AI-enabled threats or protective capabilities.
  3. 3Clear Relevance. The project clearly addresses defensive gaps against AI-enabled threats. It connects to at least one track (biosecurity, cybersecurity, or defense infrastructure) and demonstrates understanding of the threat landscape.
  4. 4Strong Contribution. The above, plus the project builds on existing defensive approaches and offers novel tools, frameworks, or implementations. It explicitly explains how it strengthens defensive capabilities with realistic deployment potential.
  5. 5Breakthrough Impact. The above, plus the project provides breakthrough insights or tools that could significantly influence defensive technology development. It identifies critical gaps and presents compelling solutions with clear paths from prototype to deployed system.

Did you build something that actually works?

Scoring guide
  1. 1Incomplete or Flawed. The project appears rushed or incomplete. Technical implementation is flawed, core functionality doesn't work, or the approach is fundamentally unsound. Little to no documentation.
  2. 2Basic Competence. The project shows reasonable effort with basic technical competence. Core functionality partially works. Documentation exists but may be incomplete. Some limitations are acknowledged.
  3. 3Solid Hackathon Project. The project is technically solid and well-scoped for 48 hours. Core functionality works and is documented. Code/methods are understandable and limitations are honestly addressed. This is what a good weekend prototype should look like.
  4. 4Impressive Implementation. The above, plus the implementation exceeds typical hackathon quality. Clear methodology, thorough documentation, and working demo. The tool/prototype is immediately useful for defenders and could realistically be built upon.
  5. 5Exceptional Execution. The project far exceeds expectations with exceptional technical execution. The implementation is elegant, fully functional, and includes something special (e.g., deployed demo, exceptional documentation, innovative architecture, or clear startup potential).

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Cite this project

@misc{banik2025raccognize,
  title = {{Raccognize - have AI companies stolen my images?}},
  author = {Leon Banik and Sandu Robert and Amelie Biewer and Alan Said Lopez Lopez},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/raccognize-have-ai-companies-stolen-my-images-wqfu}},
  url = {https://apartresearch.com/sprints/projects/raccognize-have-ai-companies-stolen-my-images-wqfu}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026