Non-destructive detection of counterfeit audio amplifier modules

Counterfeiting in the electronics industry is an increasingly challenging issue to detect. The variety of counterfeit electronics adds complexity to the problem and underscores the need for advanced detection methods. Building on previous research, this paper presents a non-destructive detection approach that utilizes electrical measurements and machine learning algorithms, which can be trained in the field during operation. This method offers a cost-effective solution to this widespread issue, particularly for simple electronic devices. The approach, which has previously demonstrated its validity on standalone amplifiers, is now being refined and applied to commercial modules that incorporate the same components, further confirming its effectiveness.