SPOOK: Sparse Pixel Optimization for Off-Manifold Knowledge in Vision Encoders

Ankit Gupta; Akshat Pandya; Christoph Adami; Emily Dolson

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arXiv · 2610.32835

SPOOK: Sparse Pixel Optimization for Off-Manifold Knowledge in Vision Encoders

Ankit Gupta·Akshat Pandya·Christoph Adami·Emily Dolson

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Abstract

Modern vision encoders are widely used as semantic representations, but the relationship between embedding similarity and human-visible semantics can become unclear far from the natural-image manifold. We probe this regime in a black-box setting by applying sparse pixel edits to blank canvases with a simple pixel-level hill-climbing procedure. Across ImageNet and Flickr30k, we evaluate whether these edited blank canvases can retrieve chosen targets in multimodal contrastive encoders and self-supervised visual encoders. On OpenCLIP, inputs optimized toward class prompts achieve strong top-1 and top-5 targeted retrieval performance; on DINOv3, inputs optimized toward class prototypes achieve high target alignment and near-top prototype ranks. These results show that sparse edits on otherwise blank, visually non-semantic inputs can enter target semantic basins in both language-aligned and self-supervised visual representation spaces. Our findings identify a shared off-manifold failure mode of modern vision encoders and suggest sparse blank-canvas optimization as a simple probe of representation geometry.

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BibTeX RIS

Ankit Gupta, Akshat Pandya, Christoph Adami, Emily Dolson. 2026-10-23. SPOOK: Sparse Pixel Optimization for Off-Manifold Knowledge in Vision Encoders. https://arxiv.org/abs/2610.32835

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