Nov. 5, 2023, 6:48 a.m. | Xiuli Bi, Bo Liu, Fan Yang, Bin Xiao, Weisheng Li, Gao Huang, Pamela C. Cosman

cs.CV updates on arXiv.org arxiv.org

As deep learning technology continues to evolve, the images yielded by
generative models are becoming more and more realistic, triggering people to
question the authenticity of images. Existing generated image detection methods
detect visual artifacts in generated images or learn discriminative features
from both real and generated images by massive training. This learning paradigm
will result in efficiency and generalization issues, making detection methods
always lag behind generation methods. This paper approaches the generated image
detection problem from a new …

arxiv deep learning detection detection methods features generated generative generative models image image detection images learn massive people technology training visual

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