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.

翻译：近年来深度神经网络应用的爆炸式增长引发了对此类模型黑箱使用的担忧，在高风险领域其透明度和可信度有限，这已具体化为欧盟《人工智能法案》等监管要求。虽然已有嵌入置信度指标的模型被提出，但此类方法无法在不重新训练的情况下应用于现有模型，限制了其广泛应用。另一方面，评估预训练模型的事后方法主要关注提升模型预测置信度，以及将检测分布外样本或对抗性攻击样本作为独立应用。为解决现有方法适用性有限的问题，我们提出了用于置信度评分的多层分析框架，这是一个统一的事后分析框架，通过分析中间层激活生成分类图。基于分类图，我们推导出适用于置信度估计、检测分布偏移和对抗性攻击的评分，将这三个问题统一于共同框架中。在使用VGG16和ViTb16模型的实验中，该框架以远低于现有先进方法的计算开销实现了超越其性能的表现。