
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.


coremsg

severity-controlled-manipulation-of-text-to-image-generative-model-biases


Severity-Controlled Manipulation of Text-to-Image Generative Model Biases


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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.


virtually-assured-amplification-attack-exposes-vulnerabilities-of-probabilistic-copyright-protection-for-text-to-image-generative-models


Virtually Assured Amplification Attack Exposes Vulnerabilities of Probabilistic Copyright Protection for Text-to-Image Generative Models
