Fault Lines in the Black Box: Using Signal Interference Theory to Expose AI Vulnerabilities
Researchers are borrowing destructive and constructive interference principles from classical signal processing to audit the hidden failure modes of neural networks. By treating model activations as overlapping wave phenomena, a new discipline of interference-based AI diagnostics is surfacing adversarial blind spots before they cause real-world harm. The implications stretch from autonomous vehicle perception systems to diagnostic medical imaging.