Naslov
A Privacy Preservation Pipeline for Personally Identifiable Data in Images Using Convolutional and Transformer Architectures

Autori
Brkić, Karla ; Hrkać, Tomislav ; Kalafatić, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of MIPRO 2022 45th Jubilee International Convention / Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2022, 1063-1068

Skup
45th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2022)

Mjesto i datum
Opatija, Hrvatska, 23.05.2022. - 27.05.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
sensitive data, de-identification, privacy protection, generative adversarial networks, transformers, machine learning, deep learning

Sažetak
Image and video data of people, shared voluntarily and involuntarily, is ubiquitous. There is an increased need for techniques that enable privacy protection via removal of personally identifiable information in such data, spurred by regulatory interest and increased social awareness of privacy implications. In this paper, we introduce a privacy preservation pipeline that enables de- identifying personal data in images and videos via replacement image synthesis while retaining data utility. We utilize the recently proposed convolutional VQGANs with autoregressive transformers to synthesize realistic and fully deidentified images of people that are then blended with the original scene. Experimental results show that the method provides strong de-identification while retaining the realism of the scene.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA