Modeling the differential response of real versus AI-generated images under secondary generative transformations yields state-of-the-art cross-generator detection on 19 unseen models.
Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794
2 Pith papers cite this work. Polarity classification is still indexing.
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Diffusion models exhibit a structural limitation when generating samples on low-dimensional feasible regions for constrained tasks, and sequential autoregressive generation using RL and MCTS improves constraint satisfaction.
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Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
Modeling the differential response of real versus AI-generated images under secondary generative transformations yields state-of-the-art cross-generator detection on 19 unseen models.
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When Diffusion Breaks Constraints: Sequential Autoregressive Generation with RL and MCTS
Diffusion models exhibit a structural limitation when generating samples on low-dimensional feasible regions for constrained tasks, and sequential autoregressive generation using RL and MCTS improves constraint satisfaction.