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Coincidental Generation. (arXiv:2304.01108v2 [cs.CV] UPDATED)
cs.CR updates on arXiv.org arxiv.org
Generative A.I. models have emerged as versatile tools across diverse
industries, with applications in privacy-preserving data sharing, computational
art, personalization of products and services, and immersive entertainment.
Here, we introduce a new privacy concern in the adoption and use of generative
A.I. models: that of coincidental generation, where a generative model's output
is similar enough to an existing entity, beyond those represented in the
dataset used to train the model, to be mistaken for it. Consider, for example,
synthetic portrait …
adoption applications art beyond commercial computational data data sharing entertainment generative immersive modeling privacy privacy concern products services sharing synthetic tools train virtual