可解释的深度伪造检测挑战
Explainable Deepfake Detection Challenge
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- Monash University（莫纳什大学）
- MBZUAI（穆罕默德·本·扎耶德人工智能大学）
- The University of Queensland（昆士兰大学）
- American University of Sharjah（沙迦美国大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
ACM多媒体2026年可解释深度伪造检测挑战基于XPlainVerse基准测试，评估图像分类及解释生成方法。参与者为图像提交真假标签与两种解释，评估结合多种指标，其成果将助力下一代可解释深度伪造检测器发展。
中文摘要 AI 辅助
深度伪造检测正从二元分类决策迈向能够解释支持这些决策的视觉证据的系统。这一转变对现实世界的验证设置很重要，不同用户不仅需要了解图像是否被操纵，还需知道为何它被视为可疑。ACM多媒体2026年的可解释深度伪造检测挑战旨在对这种联合能力进行基准测试。该挑战基于XPlainVerse构建，评估图像分类和基于自然语言的解释生成方法。参与者为每张图像提交真假标签及两种解释，评估结合分类指标与语义相似性、简单性和意图感知基础指标。通过该挑战开发的方法将有助于下一代可解释深度伪造检测器的发展。评估脚本、基线模型和相关代码可在指定网址获取。
英文摘要
Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is important for real-world verification settings, where diverse users need to understand not only whether an image is manipulated, but also why it is considered suspicious. The Explainable Deepfake Detection Challenge at ACM Multimedia 2026 is designed to benchmark this joint capability. Built on XPlainVerse, a million-scale benchmark for explainable deepfake detection, the challenge evaluates methods on image classification and grounded natural-language explanation generation. Participants submit a real/fake label together with two explanations for each image: a detailed complex explanation for technical users and a concise simple explanation for general users. The evaluation combines classification metrics with semantic similarity, simplicity, and intent-aware grounding metrics that assess whether explanations identify the relevant manipulated entities and supporting visual evidence. The methodologies developed through the challenge will contribute to the development of next-generation explainable deepfake detectors. Evaluation script, baseline models, and accompanying code are available on https://github.com/Abhijeet8901/XPlainVerse-ACMChallenge.
