Lightweight Detection of Out-of-Distribution and Adversarial Samples via Channel Mean Discrepancy

• URL: http://arxiv.org/abs/2104.11408v1
• Date: Fri, 23 Apr 2021 04:15:53 GMT
• Title: Lightweight Detection of Out-of-Distribution and Adversarial Samples via Channel Mean Discrepancy
• Authors: Xin Dong, Junfeng Guo, Wei-Te Ting, H.T. Kung
• Abstract summary: We introduce Channel Mean Discrepancy (CMD), a model-agnostic distance metric for evaluating the statistics of features extracted by classification models. We experimentally demonstrate that CMD magnitude is significantly smaller for legitimate samples than for OOD and adversarial samples. Preliminary results show that our simple yet effective method outperforms several state-of-the-art approaches to detecting OOD and adversarial samples.
• Score: 14.103271496247551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting out-of-distribution (OOD) and adversarial samples is essential when deploying classification models in real-world applications. We introduce Channel Mean Discrepancy (CMD), a model-agnostic distance metric for evaluating the statistics of features extracted by classification models, inspired by integral probability metrics. CMD compares the feature statistics of incoming samples against feature statistics estimated from previously seen training samples with minimal overhead. We experimentally demonstrate that CMD magnitude is significantly smaller for legitimate samples than for OOD and adversarial samples. We propose a simple method to reliably differentiate between legitimate samples from OOD and adversarial samples using CMD, requiring only a single forward pass on a pre-trained classification model per sample. We further demonstrate how to achieve single image detection by using a lightweight model for channel sensitivity tuning, an improvement on other statistical detection methods. Preliminary results show that our simple yet effective method outperforms several state-of-the-art approaches to detecting OOD and adversarial samples across various datasets and attack methods with high efficiency and generalizability.

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