Implicit Device Tracking under the Multimedia Technology Wave

The proliferation of Android multimedia applications highlights the critical role of mobile sensors. Inherent manufacturing defects enable implicit device identification without consent, facilitating covert tracking. This bolsters security through reliable malicious actor tracking, unlike spoof-vulnerable explicit methods. However, prior sensor-based identification suffers from signal noise and device degradation, compromising robustness.In this paper, we propose AMSensorFP, a novel Android implicit device tracking framework. We develop an application to collect device information and multi-sensor data to construct device fingerprints. Subsequently, we build a dataset by performing pairwise difference calculations on the collected fingerprints. And then enhance the dataset with Gaussian noise to improve data diversity and robustness. An autoencoder reduces feature dimensionality, and the processed features are fed into a BiLSTM model with a multi-head attention mechanism, enabling effective fingerprint recognition. Experimental results show that AMSensorFP achieves 99.88% accuracy and 97.34% true positive rate(TPR), significantly outperforming existing methods. Ablation analysis further highlights the contributions of each module and feature in the framework. AMSensorFP delivers a reliable solution for device tracking and security enhancement.