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The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
April 24, 2024, 4:44 a.m. | Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, Bo Luo
cs.CV updates on arXiv.org arxiv.org
Abstract: Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photography devices or created by human artists (i.e., real images). While the outstanding performance of such generative models is generally well received, security concerns arise. For instance, such image generators could be used to facilitate fraud or scam schemes, generate and spread misinformation, or produce fabricated artworks. In this paper, we present …
abstract adversarial adversarial ai ai models art artists arxiv benchmarking concerns cs.ai cs.cr cs.cv detection devices generated generative generative ai models generative models human images optical performance photography prompts quality security security concerns text type understanding
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