Brain-inspired perception-decision machine for fake speech detection.

Area of Science:

• Computer Science
• Artificial Intelligence
• Signal Processing

Background:

• Artificial Intelligence Generated Content (AIGC) presents evolving challenges for fake speech detection.
• Existing classification-based methods struggle with generalization to novel audio spoofing techniques and require extensive training data.

Purpose of the Study:

• To develop a robust and adaptable fake speech detection system.
• To overcome the limitations of current methods in handling diverse and unseen audio forgery types.

Main Methods:

• Introduction of a brain-inspired, multi-clue detection paradigm.
• A perception-decision machine with independent detectors optimized for Maximum Detection Precision (MaxDP).
• A decision-making module using logical reasoning and a variable-length OR operation for incremental learning.

Main Results:

• The proposed framework demonstrates effectiveness in detecting audio forgeries.
• The multi-clue approach shows improved generalization capabilities compared to traditional methods.
• The system facilitates seamless incremental learning of new forgery clues without complete retraining.

Conclusions:

• The multi-clue detection perspective offers a promising direction for fake speech detection.
• The proposed framework enhances explainability and practical adaptability to emerging audio threats.
• This approach represents a significant advancement in combating sophisticated AI-generated audio deception.