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SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake Detection

Authors:

Boris Joukovsky,

José Oramas Mogrovejo,

Tinne Tuytelaars,

Nikos DeligiannisAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 8

Article No.: 253, Pages 1 - 29

https://doi.org/10.1145/3665248

Published: 13 June 2024 Publication History

Abstract

Explainable Artificial Intelligence (XAI) attempts to help humans understand machine learning decisions better and has been identified as a critical component toward increasing the trustworthiness of complex black-box systems, such as deep neural networks. In this article, we propose a generic and comprehensive framework named SNIPPET and create a user interface for the subjective evaluation of visual explanations, focusing on finding human-friendly explanations. SNIPPET considers human-centered evaluation tasks and incorporates the collection of human annotations. These annotations can serve as valuable feedback to validate the qualitative results obtained from the subjective assessment tasks. Moreover, we consider different user background categories during the evaluation process to ensure diverse perspectives and comprehensive evaluation. We demonstrate SNIPPET on a DeepFake face dataset. Distinguishing real from fake faces is a non-trivial task even for humans that depends on rather subtle features, making it a challenging use case. Using SNIPPET, we evaluate four popular XAI methods which provide visual explanations: Gradient-weighted Class Activation Mapping, Layer-wise Relevance Propagation, attention rollout, and Transformer Attribution. Based on our experimental results, we observe preference variations among different user categories. We find that most people are more favorable to the explanations of rollout. Moreover, when it comes to XAI-assisted understanding, those who have no or lack relevant background knowledge often consider that visual explanations are insufficient to help them understand. We open-source our framework for continued data collection and annotation at https://github.com/XAI-SubjEvaluation/SNIPPET.

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Cited By

Hu WYang YHu H(2024)Pseudo Label Association and Prototype-Based Invariant Learning for Semi-Supervised NIR-VIS Face RecognitionIEEE Transactions on Image Processing10.1109/TIP.2024.336453033(1448-1463)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3364530
Yang YHu WHu H(2024)Unsupervised NIR-VIS Face Recognition via Homogeneous-to-Heterogeneous Learning and Residual-Invariant EnhancementIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334617619(2112-2126)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3346176

Index Terms

SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies
  2. Visualization
    1. Visualization design and evaluation methods

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 8

August 2024

726 pages

EISSN:1551-6865

DOI:10.1145/3618074

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Association for Computing Machinery

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Publication History

Published: 13 June 2024

Online AM: 22 May 2024

Accepted: 12 May 2024

Revised: 03 May 2024

Received: 15 September 2023

Published in TOMM Volume 20, Issue 8

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Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen
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Cited By

Hu WYang YHu H(2024)Pseudo Label Association and Prototype-Based Invariant Learning for Semi-Supervised NIR-VIS Face RecognitionIEEE Transactions on Image Processing10.1109/TIP.2024.336453033(1448-1463)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3364530
Yang YHu WHu H(2024)Unsupervised NIR-VIS Face Recognition via Homogeneous-to-Heterogeneous Learning and Residual-Invariant EnhancementIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334617619(2112-2126)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3346176

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