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Found 3 papers, 1 papers with code
Critical windows: non-asymptotic theory for feature emergence in diffusion models
Additionally, preliminary experiments on Stable Diffusion suggest critical windows may serve as a useful tool for diagnosing fairness and privacy violations in real-world diffusion models.
Pandora's White-Box: Increased Training Data Leakage in Open LLMs
In fine-tuning, we find that given access to the loss of the fine-tuned and base models, a fine-tuned loss ratio attack FLoRA is able to achieve near perfect MIA peformance.
MoPe: Model Perturbation-based Privacy Attacks on Language Models
In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training data of a pre-trained language model, given white-box access to the models parameters.
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