LatentStealth is a latent-space optimization method that produces imperceptible yet effective adversarial attacks on expressive human pose and shape estimation models using few output queries.
arXiv preprint arXiv:2504.17219 (2025)
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Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.
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LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation
LatentStealth is a latent-space optimization method that produces imperceptible yet effective adversarial attacks on expressive human pose and shape estimation models using few output queries.
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Understanding Latent Diffusability via Fisher Geometry
Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.