🤖 AI Summary
This study addresses the high false positive rates and lack of rigorous validation mechanisms in current inductive AI models for detecting synthetic media in forensic contexts. To overcome these limitations, the work introduces abductive reasoning into judicial-grade synthetic media detection for the first time, constructing a fact matrix that synergistically integrates multiple probabilistic detection models with state-of-the-art watermarking techniques such as OpenAI’s SynthID. This approach enables mutual corroboration among diverse detection outputs, significantly reducing false positives while maintaining high true positive recall. The paper also presents the first empirical evaluation of SynthID, revealing complementary strengths among different detection modalities and offering a reliable, interpretable technical pathway suitable for forensic applications.
📝 Abstract
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.