Brain-inspired perception-decision machine for fake speech detection

The rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies challenges fake speech detection with an ever-evolving diversity of spoofed audio. Current approaches, which rely on a classification-based perspective, are highly dependent on a big amount of training data and show limited generalization to unseen attack types. To address these limitations, this paper introduces a brain-inspired, multi-clue detection paradigm. We propose a perception-decision machine composed of two core components. The perception module utilizes multiple independent detectors, each optimized for Maximum Detection Precision (MaxDP) to identify a specific forgery clue. By standardizing their outputs into binary Boolean values, this design allows for flexible computational models. The decision-making module then renders a final judgment by first evaluating learned combinations of the detected clues through a logical reasoning process. The outcomes of this reasoning are then aggregated using a variable-length OR operation, a mechanism that enables the seamless incremental learning of new forgery clues without retraining the entire system. Our results validate the effectiveness of the multi-clue detection perspective, demonstrating the framework's potential for enhanced explainability and practical adaptability to new threats.