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Found 4 papers, 2 papers with code
Disrupting Style Mimicry Attacks on Video Imagery
We show that while anti-mimicry tools can offer protection when applied to individual frames, this approach is vulnerable to an adaptive countermeasure that removes protection by exploiting randomness in optimization results of consecutive (nearly-identical) frames.
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
In this paper, we show that poisoning attacks can be successful on generative models.
TMI! Finetuned Models Leak Private Information from their Pretraining Data
In this work we propose a new membership-inference threat model where the adversary only has access to the finetuned model and would like to infer the membership of the pretraining data.
How to Combine Membership-Inference Attacks on Multiple Updated Models
Our results on four public datasets show that our attacks are effective at using update information to give the adversary a significant advantage over attacks on standalone models, but also compared to a prior MI attack that takes advantage of model updates in a related machine-unlearning setting.
