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аналитика - Text-to-image generative models
Severity-Controlled Manipulation of Text-to-Image Generative Model Biases
This work proposes a computationally efficient technique to manipulate the biases of text-to-image generative models by targeting the embedded language models. The method enables precise control over the severity of output manipulation through vector algebra-based embedding interpolation and extrapolation.
Virtually Assured Amplification Attack Exposes Vulnerabilities of Probabilistic Copyright Protection for Text-to-Image Generative Models
The proposed Virtually Assured Amplification Attack (VA3) framework significantly amplifies the probability of generating copyright-infringing content on text-to-image generative models with probabilistic copyright protection mechanisms.
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