Recent advancement in sensor technology facilitates having a thermal camera at a lower price. These cameras have many potential applications but can also be used for malicious purposes, such as capturing user interfaces and retrieving user information from heat traces in the thermal images. This dataset is created during an interactive study investigating the threat of thermal attacks on user interfaces. We adapted the following experimental setup during data collection. 2 camera perspectives- FLIR E8-XT camera placed behind the participant and Optris PI 450i camera placed left of the participant. 4 types of input devices- i) smartphone, ii) 3 keyboards- a PBT keyboard, an ABS keyboard, and a metal frame keyboard 3 types of user input data (text, email address, password) In summary, we have collected 1152 images from the FLIR camera and another 1152 images from the Optris camera through an interactive study with 32 participants. For each participant, we captured 36 images (9 types of user input, 4 types of input devices). The created dataset can be used to evaluate the deep learning model developed to prevent thermal imaging attacks. Furthermore, the ground truth user input of text, email address, and passwords are structured along with the corresponding image ID so that the advanced data-driven model can be employed to identify user input and investigate the type of user input that can be easily cracked using machine learning techniques.

This work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1, an EPSRC New Investigator award (EP/V008870/1), the Royal Society of Edinburgh (Award number 65040).