## 1 Introduction  
Recently, mobile devices have become an integral part of daily life. These mobile devices are utilized to run different types of applications, including video calling, web browsing, gaming, navigation; hence, energy-efficient processing on these battery-empowered mobile devices is of utmost importance [1, 2]. Mobile cloud computing, where most of the computations happen in the cloud (also known as _Cloud Offloading_) [3], is considered to be a potential solution for energy-efficient processing. However, application processing that needs privacy and security, such as a banking app or a secure data storage app, is often processed on the mobile device instead of cloud offloading. Moreover, as mobile edge computing becomes more and more ubiquitous, security issues in these mobile devices become more paramount. Such mobile devices have to face hostile security threats [4, 5, 6, 7], such as physical, logical/software-based, and side-channel/lateral attacks. Amongst these, side-channel attack [4, 5, 6] is a popular security threat due to ease of access to the physical hardware where attacks are performed by observing the properties and behavior of the system, such as power consumption, thermal dissipation, electromagnetic emission, etc. Comparatively, a lot less documented studies are performed in temperature (thermal)-based side channel attacks [4, 5, 6] in mobile edge devices. Most of these mobile devices come equipped with heterogeneous multi-processors systems-on-chip (MPSoC), which consists of multiple heterogeneous processors on a single chip, capable of processing different types of applications to cater for performance and energy-efficiency of the executing applications. Due to an increase in the usage of MPSoCs [8, 9, 10, 11, 12, 13] in mobile edge devices and a rise in studies on thermal side channel attacks [6, 14, 15], it is crucial that side channel attacks in such a platform should be addressed with utmost importance [15]. To explore feasibility of thermal side-channel attack in a real commercial mobile device, we designed a new type of attack which involved computer vision-based ConvolutionalNeural Network (CNN). Among all the fields of Neural Network-based machine learning and pattern recognition, computer vision-based Neural Networks, especially CNNs and Deep Learning (DL) [16; 17], are well studied and mature comparatively. Recently, CNN models have achieved high prediction accuracy in applicative fields to solve several real-life challenges, such as traffic categorization [18; 19], human rights violation [20], weather forecasting [21], etc. Given the high success rate in understanding patterns, we utilized a CNN model-based attack. To perform the attack, we chose 4 of the 25 most common passwords of 2017 and 2018 [22; 23] as surveyed by the Internet security firm SplashData. The 4 common passwords used by the user, which are chosen for our attack, are _123456_, _passwod_, _111111_, and _football_. We then executed AES-256 [24] encryption on a text file using the aforementioned passwords on Odroid XU4 development board [25] running on ondemand Linux governor [26] and recorded the thermal behavior of the CPUs. We trained ResNet model [27], a pre-trained CNN model trained on ImageNet using _transfer learning_, with the graphical representation of the thermal behavior (as shown in Figures 0(a) and 0(a)). ResNet was able to achieve a training prediction accuracy of 46.88% and a testing prediction accuracy of 31.99%, which means that ResNet is able to predict the correct password, one out of every four attempts on an average. Figures 0(b) and 0(b) show the region of interest on the graphical representation of the thermal behavior which is used by the CNN model to predict the password. In order to determine whether ResNet is classifying the thermal data based on the features of the thermal peaks, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) [28] to visualize in which areas of the graphical data the CNN was focusing on to predict the password being used for encryption process.