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Machine learning-based mobile device in-air signature authentication
In the last decade, people have been surrounded by mobile devices such as smartphones, smartwatches, laptops, smart TVs, tablets, and IoT devices. As sensitive personal information such as photos, messages, contact information, schedules, and bank accounts are all stored on mobile devices today, the security and protection of such personal information are becoming more and more important. Today’s mobile devices are equipped with a variety of embedded sensors such as accelerometer, gyroscope, magnetometer, camera, GPS sensor, acoustic sensors, etc. that produce raw data on location, motion, and the environment around us. Based on these sensor data, we propose novel in-air signature authentication technologies on both smartphone and smartwatch in this dissertation. In-air signature authentication, as an essential behavioral biometric trait, has been adopted for identity verification and user authorization, as well as the development of deep neural networks, has vastly facilitated this field. This dissertation examines two challenging problems. One problem is how to deploy machine learning techniques to authenticate user in-air signatures in more convenient, intuitive, and secure ways by using smartphone and smartwatch in daily settings. Another problem is how to deal with the limited computational resources on today’s mobile devices which restrict to use machine learning models due to the substantial computational costs introduced by millions of parameters.
To address the two above problems separately, we conduct the following research works. 1) The first work AirSign leverages both in-built acoustic and motion sensors on today’s smartphone for user authentication by signing signatures in the air without requiring any special hardware. This system actively transmits inaudible acoustic signals from the earpiece speaker, receives echoes back through both in-built microphones to “illuminate” signature and hand geometry, and authenticates users according to the unique features extracted from echoes and motion sensors. 2) The second work DeepWatchSign leverages in-built motion sensors on today’s smartwatch for user in-air signature authentication. The system adopts LSTM-AutoEncoder to generate negative signature data automatically from the enrolled signatures and authenticates each user by the deep neural network model. 3) We close this dissertation with an l0-based sparse group lasso approach called MobilePrune which can compress the deep learning models for both desktop and mobile platforms. This approach adopts group lasso penalty to enforce sparsity at the group level to benefit General Matrix Multiply (GEMM) and optimize the l0 norm in an exact manner. We observe the substantial reduction of compression ratio and computational costs for deep learning models. This method also achieves less response delay and battery consumption on mobile devices.
- Doctor of Philosophy
- Computer Science
- West Lafayette