This course teaches learners how gyroscopes in smartphones can be used to eavesdrop on speech in the vicinity of the phone, even without microphone access. The course covers topics such as the susceptibility of gyroscopes to sound, speech analysis using gyroscopes, gender and speaker identification, isolated words recognition, and practical compromises for eavesdropping on multiple devices. The teaching method includes theoretical explanations, experimental setups, speech analysis techniques, and defense mechanisms. The intended audience for this course includes individuals interested in cybersecurity, mobile device security, and privacy protection.
Overview
Syllabus
Intro
INTRODUCTION - YAN MICHALEVSKY
MICROPHONE ACCESS
GYROSCOPE ACCESS BY A BROWSER JAVASCRIPT CODE EXAMPLE
MEMS GYROSCOPES
GYROSCOPES ARE SUSCEPTIBLE TO SOUND
GYROSCOPES ARE (LOUSY, BUT STILL) MICROPHONES
SOFTWARE LIMITATION OF THE SAMPLING RATE
SAMPLING FREQUENCY LIMITS
THE EFFECTS OF LOW SAMPLING FREQUENCY SPEECH SAMPLED AT 8,000HZ
WE CAN SENSE HIGH FREQUENCY SIGNALS DUE TO ALIASING
EXPERIMENTAL SETUP
SPEECH ANALYSIS USING A SINGLE GYROSCOPE
PREPROCESSING • All samples are converted to audio files in WAV format
FEATURES • MFCC-Mel-Frequency Cepstral Coefficients • Statistical features are used mean and variance
CLASSIFIERS
DYNAMIC TIME WARPING
GENDER IDENTIFICATION
WE CAN SUCCESSFULLY IDENTIFY GENDER
SPEAKER IDENTIFICATION
A GOOD CHANCE TO IDENTIFY THE SPEAKER
ISOLATED WORDS RECOGNITION SPEAKER INDEPENDENT
HOW CAN WE LEVERAGE EAVESDROPPING SIMULTANEOUSLY ON TWO DEVICES?
SIMILAR TO TIME-INTERLEAVED ADC's
NON-UNIFORM RECONSTRUCTION REQUIRES KNOWING PRECISE TIME-SKEWS
PRACTICAL COMPROMISE Interleaving samples from multiple devices
EVALUATION Tested for the case of speaker dependent word recognition
FURTHER ATTACKS
SOURCE SEPARATION
AMBIENT SOUND RECOGNITION
SOFTWARE DEFENSES
HARDWARE DEFENSES
CONCLUSION
Taught by
Black Hat
