1 research outputs found
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement
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.Comment: 15 pages, 6 figure