# Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling Source: [Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:58.137Z Authors: Ethan Wisdom, Tejas Gokhale, Chaowei Xiao, Yezhou Yang Published: 2023-03-30T00:59:37.000Z ## Abstract In this work, we present a data poisoning attack that confounds machine learning models without any manipulation of the image or label. This is achieved by simply leveraging the most confounding natural samples found within the training data itself, in a new form of a targeted attack coined "Mole Recruitment." We define moles as the training samples of a class that appear most similar to samples of another class, and show that simply restructuring training batches with an optimal number of moles can lead to significant degradation in the performance of the targeted class. We show the efficacy of this novel attack in an offline setting across several standard image classification datasets, and demonstrate the real-world viability of this attack in a continual learning (CL) setting. Our analysis reveals that state-of-the-art models are susceptible to Mole Recruitment, thereby exposing a previously undetected vulnerability of image classifiers. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()