## 1 Introduction  
The availability of high-precision sensors such as cameras, accelerometers and microphones on modern mobile devices afford users a wide range of functionality such as navigation, virtual assistants and even pedometers. While on-board sensors enable rich user experiences, these sensors can be exploited by malicious applications to monitor the user in unintended ways by tracking transducer signals emanating from the device, such as electrical, sound and vibration signals, and often contain information about the device processes, operation and user interactions. These \"collateral\" signals have significant implications in the field of cyber-security and have been used to bypass cryptographic algorithms such as RSA (Rivest-Shamir-Adleman) [1] and exploit acoustic information to extract sensitive information such as retrieve user PIN codes [2] and passwords [3]. Touch-screens, the de facto user input interface, take up a significant portion of the device physical surface. Consequently, user interactions with these touchscreens generate non-negligible signals that can be recorded by on-board sensors. On-board motion sensors such as the gyroscope and accelerometer found on mobile devices are particularly sensitive to force and direction changes when a user interacts with the device by tapping the screen. Analysing the gyroscope and accelerate sensor readings, off-the shelf mobile applications (apps) such as TouchLogger are able to infer user text inputs on various mobile operating systems (iOS and Android) with different device form factors and physical design. Extending this work on hardware sensors, ACCessory is another application built to evaluate text input using a predictive model to infer character sequences from accelerometer data with supervised learning techniques [4]. Earlier studies analysed acoustic excitation to retrieve user input on non-touchscreen devices such as physical computer keyboards. It was demonstrated that keystroke inference can be performed using multiple microphones with relatively high accuracy [5] even when facilitated via Voice-over-IP (VoIP) services such as Skype [6]. Consequently, it is conceivable that such unauthorized audio recording can be used to recover sensitive user information, using inter-keystroke timing or statistical analysis to recover typed text [7] or even ten-character passwords within 20 attempts [8]. While some practitioners may dismiss the use of acoustic signals as a possible security loophole on mobile devices [9], recent publications [10; 11; 12; 13; 14] show that acoustic techniques such as tracking the Doppler effect, supplying an external excitation signal and Time Difference of Arrival (TDOA) that can be used to retrieve text input on physical keyboard can be adapted to compromise mobile devices as well. One such system, SonarSnoop utilises an active acoustic technique by emitting human inaudible acoustic signals and recording the echo to profile user interaction and infer touchscreen unlock patterns [14]. Comparably, passive techniques like TDOA which calculate the time difference between the reception of the signal by different transducers to infer input location can be further enriched with acoustic frequency analysis to distinguish touch input [13]. In this study, we apply machine learning techniques to predict the touchscreen input location from touchscreen device interfaces via acoustic fingerprints collected from on-board mechano-acoustic transducers. An acoustic fingerprint is a summary of acoustic features extracted from an acoustic signal that can identify similar acoustic events [15]. Acoustic fingerprinting is often combined with statistical methods to identify similar types of sounds quickly and has seen application in a broad range of arenas from identifying pop music [16] to determining volcanic eruptions which inject ash into the tropopause [17].