Squint Your Eyes: Choosing When to Blur for Sparse Visual Domains

Visual recognition is typically studied under shifts in style or corruption, but the effect of spatially disconnected visual evidence remains underexplored. We study visual sparsity, where objects are depicted as separate dots, symbols, or line fragments. Motivated by the human tendency to squint when encountering fragmented visual patterns, we investigate whether blurring helps recognition under sparsity. We conduct a controlled human study and an analogous model evaluation. Blur improves recognition under sparsity for both humans and modern recognition models, but the relative improvement is substantially larger for models, indicating that they are more vulnerable to sparsity. This human–model mismatch creates an exploitable gap for moderation attacks, where sparse images can remain recognizable to people yet be substantially harder for moderation systems. We demonstrate this failure mode in a content-moderation setting. To study sparsity systematically, we introduce DN AbstraGrid, a benchmark based on Mini DomainNet, that spans multiple abstraction levels: a sparse target partition, a non-sparse partition, and the original domains. The latter two serve as control domains where performance should be preserved. Blur strongly benefits the sparse partition while harming the remaining domains, showing that it should be applied adaptively rather than uniformly. To this end, we propose Squint!, a lightweight, model-agnostic module that predicts when and how much to blur each image. It is based on a compact radial Fourier power-spectrum descriptor, trained with weak supervision and not on the target sparse domains. Squint! improves recognition on sparse domains while preserving performance on the controls. A single plug-and-play Squint! model improves performance across 12 VLMs and 7 domain-generalization methods, while also improving recognition of sparse moderation attacks.