Robust Nearest Neighbour Retrieval Using Targeted Manifold Manipulation

• URL: http://arxiv.org/abs/2511.06261v2
• Date: Wed, 12 Nov 2025 01:51:06 GMT
• Title: Robust Nearest Neighbour Retrieval Using Targeted Manifold Manipulation
• Authors: B. Ghosh, H. Harikumar, S. Rana,
• Abstract summary: Nearest-neighbour retrieval is central to classification and explainable-AI pipelines.<n>We propose Targeted Manifold Manipulation-Nearest Neighbour (TMM-NN), which reconceptualises retrieval by assessing how readily each sample can be nudged into a designated region of the feature manifold.<n>TMM-NN implements this through a lightweight, query-specific trigger patch.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nearest-neighbour retrieval is central to classification and explainable-AI pipelines, but current practice relies on hand-tuning feature layers and distance metrics. We propose Targeted Manifold Manipulation-Nearest Neighbour (TMM-NN), which reconceptualises retrieval by assessing how readily each sample can be nudged into a designated region of the feature manifold; neighbourhoods are defined by a sample's responsiveness to a targeted perturbation rather than absolute geometric distance. TMM-NN implements this through a lightweight, query-specific trigger patch. The patch is added to the query image, and the network is weakly ``backdoored'' so that any input with the patch is steered toward a dummy class. Images similar to the query need only a slight shift and are classified as the dummy class with high probability, while dissimilar ones are less affected. By ranking candidates by this confidence, TMM-NN retrieves the most semantically related neighbours. Robustness analysis and benchmark experiments confirm this trigger-based ranking outperforms traditional metrics under noise and across diverse tasks.

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