Privacy-Preserving Video Conferencing via Thermal-Generative Images (preprint)

Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to guide the synthesis of RGB images, bringing a promising solution for real-time video conferencing when privacy leakage is a concern. SPADE-SR (Spatially-Adaptive De-normalization with Self Resampling), a variant of SPADE, is adopted to incorporate the spatial property of a thermal heatmap and the non-thermal property of a normal, privacy-free pre-recorded RGB image provided in a form of latent code. We create a PAIR-LRT-Human (LRT = Low-Resolution Thermal) dataset to validate our claims. The result enables a convenient way of video conferencing where users no longer need to groom themselves and tidy up backgrounds for a short meeting. Additionally, it allows a user to switch to a different appearance and background during a conference.

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021 (preprint)

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was “Networking Data and Intelligent Management in the Post-COVID19 Era.”