  Voice-Controllable Devices (VCDs) have seen an increasing trend towards their
adoption due to the small form factor of the MEMS microphones and their easy
integration into modern gadgets. Recent studies have revealed that MEMS
microphones are vulnerable to audio-modulated laser injection attacks. This
paper aims to develop countermeasures to detect and prevent laser injection
attacks on MEMS microphones. A time-frequency decomposition based on discrete
wavelet transform (DWT) is employed to decompose microphone output audio signal
into n + 1 frequency subbands to capture photo-acoustic related artifacts.
Higher-order statistical features consisting of the first four moments of
subband audio signals, e.g., variance, skew, and kurtosis are used to
distinguish between acoustic and photo-acoustic responses. An SVM classifier is
used to learn the underlying model that differentiates between an acoustic- and
laser-induced (photo-acoustic) response in the MEMS microphone. The proposed
framework is evaluated on a data set of 190 audios, consisting of 19 speakers.
The experimental results indicate that the proposed framework is able to
correctly classify $98\%$ of the acoustic- and laser-induced audio in a random
data partition setting and $100\%$ of the audio in speaker-independent and
text-independent data partition settings.
