用反事实推理提升伪造媒体检测准确率，大幅降低误报。
Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection
- 采用反事实推理融合多种检测方法，从事实矩阵中推断最可能结论。
- 在真实场景下将误报率显著降低，同时保持高召回率。
- 首次实证评估SynthID效果，验证多方法互补性，适合司法取证场景。
人工智能模型本质上通过概率行为将大规模数据中的通用规律应用于具体情境，这种归纳方法与从前提推导结论的演绎推理形成对比。然而研究表明，利用AI进行演绎推理存在挑战，且在实际应用中未必可行。本文提出一种替代方案：借助反事实推理，通过验证多个方法输出的一致性，从事实矩阵中识别最可能的结论。我们将该方法应用于司法领域的伪造媒体检测，发现能显著降低误报率，同时保持较高的真阳性召回率。此外，我们首次对OpenAI推出的SynthID在合成图像上的实际效果进行了实证评估，并分析了不同检测方法之间的互补性。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behaviour. This inductive approach stands in contrast to deductive reasoning approaches which seek to prove conclusions from their premises. However, research has shown that deductive reasoning with AI models is a challenging problem and in the real-world it may not always be feasible. An alternative way forward is to leverage abductive reasoning, seeking to corroborate the output of multiple approaches to identify the most likely conclusion from the factual matrix. We apply this to synthetic media detection in forensic settings, and find we are able to disproportionately lower the risk of false positives to true positive recall. We also provide the first empirical evaluation of OpenAI's rollout of SynthID on synthetic images and evaluate how complementary different synthetic media detection approaches are.
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