In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep learning based models with a huge number of tunable parameters. Recently, prototype-based methods have emerged as a promising approach to make deep learning interpretable. We particularly focus on the analysis of deepfake videos in a forensics context. Although prototype-based methods have been introduced for the detection of deepfake videos, their use in real-world scenarios still presents major challenges, in that prototypes tend to be overly similar and interpretability varies between prototypes. This paper proposes a Visual Analytics process model for prototype learning, and, based on this, presents ProtoExplorer, a Visual Analytics system for the exploration and refinement of prototype-based deepfake detection models. ProtoExplorer offers tools for visualizing and temporally filtering prototype-based predictions when working with video data. It disentangles the complexity of working with spatio-temporal prototypes, facilitating their visualization. It further enables the refinement of models by interactively deleting and replacing prototypes with the aim to achieve more interpretable and less biased predictions while preserving detection accuracy. The system was designed with forensic experts and evaluated in a number of rounds based on both open-ended think aloud evaluation and interviews. These sessions have confirmed the strength of our prototype based exploration of deepfake videos while they provided the feedback needed to continuously improve the system.

翻译：在高风险场景中，能够提供人类可解释预测的机器学习模型至关重要。随着具有海量可调参数的复杂深度学习模型的出现，这一需求尤为凸显。近年来，基于原型的方法已成为实现深度学习可解释性的重要途径。我们特别关注取证场景中深度伪造视频的分析。尽管原型方法已被引入深度伪造视频检测领域，但在实际应用中仍面临重大挑战：原型之间往往过于相似，且不同原型的可解释性存在差异。本文提出了面向原型学习的视觉分析流程模型，并据此构建了ProtoExplorer系统——一个用于原型驱动深度伪造检测模型探索与精炼的视觉分析系统。该系统提供视频数据中基于原型预测的可视化与时序过滤工具，有效解耦了时空原型处理的复杂性，便于原型可视化。进一步地，用户可通过交互式删除与替换原型精炼模型，在保持检测准确率的同时实现更可解释、更少偏差的预测。系统经法医专家设计，并通过开放式出声思维评估与访谈开展了多轮评估。这些评估既验证了基于原型方法探索深度伪造视频的有效性，也为系统的持续改进提供了反馈依据。