提出统一框架MACS，无需重训练即可检测异常样本与误分类。
Multi-Layer Confidence Scoring for Detection of Out-of-Distribution Samples, Adversarial Attacks, and In-Distribution Misclassifications
- 通过分析中间层激活生成分类图，实现后验置信度评分。
- 在VGG16和ViTb16上超越现有方法，计算开销仅为几分之一。
- 适合已部署模型的可信性增强，尤其适用于高风险场景。
深度神经网络应用的爆炸式增长引发了对模型黑箱特性的担忧，尤其在高风险领域中缺乏透明度与可信度，这已催生如欧盟人工智能法案等监管要求。尽管已有嵌入置信度度量的模型，但这些方法无法应用于未经重新训练的现有模型，限制了其广泛应用。相比之下，后验方法虽能评估预训练模型，但通常将分布外检测、对抗攻击识别和误分类检测视为独立任务。为解决现有方法适用性有限的问题，本文提出多层置信度评分框架MACS，通过分析中间层激活生成分类图，从中推导出统一的置信度评分，可同时用于检测分布外样本、对抗攻击及分布内误分类。实验表明，该方法在VGG16和ViTb16模型上性能优于当前最先进方法，且计算开销仅为几分之一。
原文摘要 · Abstract (English)
The recent explosive growth in Deep Neural Networks applications raises concerns about the black-box usage of such models, with limited trasparency and trustworthiness in high-stakes domains, which have been crystallized as regulatory requirements such as the European Union Artificial Intelligence Act. While models with embedded confidence metrics have been proposed, such approaches cannot be applied to already existing models without retraining, limiting their broad application. On the other hand, post-hoc methods, which evaluate pre-trained models, focus on solving problems related to improving the confidence in the model's predictions, and detecting Out-Of-Distribution or Adversarial Attacks samples as independent applications. To tackle the limited applicability of already existing methods, we introduce Multi-Layer Analysis for Confidence Scoring (MACS), a unified post-hoc framework that analyzes intermediate activations to produce classification-maps. From the classification-maps, we derive a score applicable for confidence estimation, detecting distributional shifts and adversarial attacks, unifying the three problems in a common framework, and achieving performances that surpass the state-of-the-art approaches in our experiments with the VGG16 and ViTb16 models with a fraction of their computational overhead.
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