Summary of Breaking Free: How to Hack Safety Guardrails in Black-box Diffusion Models!, by Shashank Kotyan et al.
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!
by Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen, Danilo Vasconcellos Vargas
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes EvoSeed, a novel algorithmic framework for generating photo-realistic natural adversarial samples that can be exploited by deep neural networks. Unlike existing approaches that rely on the white-box nature of these networks, EvoSeed operates in a black-box setting using an evolutionary strategy-based algorithm. The framework employs Conditional Diffusion and Classifier models to generate high-quality images that are misclassified by safety classifiers, raising concerns about generating harmful content. This research highlights the limitations of current safety mechanisms and the risk of plausible attacks against classifier systems using image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to create fake pictures that can trick artificial intelligence (AI) into thinking they’re real. The AI is designed to recognize images, but sometimes it can be fooled by these fake pictures. The researchers developed an algorithm called EvoSeed that creates high-quality, realistic fake pictures that the AI might mistake for real ones. This has implications for how we keep AI safe and secure from misuse. |
Keywords
* Artificial intelligence * Diffusion * Image generation
