TY - GEN
T1 - Classification of Screenshot Image Captured in Online Meeting System
AU - Kuribayashi, Minoru
AU - Kamakari, Kodai
AU - Funabiki, Nobuo
N1 - Funding Information:
Acknowledgment. This research was supported by JSPS KAKENHI Grant Number 19K22846, JST SICORP Grant Number JPMJSC20C3, and JST CREST Grant Number JPMJCR20D3, Japan.
Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022
Y1 - 2022
N2 - Owing to the spread of the COVID-19 virus, the online meeting system has become popular. From the security point of view, the protection against information leakage is important, as confidential documents are often displayed on a screen to share the information with all participants through the screen sharing function. Some participants may capture their screen to store the displayed documents in their local devices. In this study, we focus on the filtering process and lossy compression applied to the video delivered over an online meeting system, and investigate the identification of screenshot images using deep learning techniques to analyze the distortion caused by such operations. In our experimental results for Zoom applications, we can obtain more than 92.5% classification accuracy even if the captured image is intentionally edited to remove the traces of screen capture.
AB - Owing to the spread of the COVID-19 virus, the online meeting system has become popular. From the security point of view, the protection against information leakage is important, as confidential documents are often displayed on a screen to share the information with all participants through the screen sharing function. Some participants may capture their screen to store the displayed documents in their local devices. In this study, we focus on the filtering process and lossy compression applied to the video delivered over an online meeting system, and investigate the identification of screenshot images using deep learning techniques to analyze the distortion caused by such operations. In our experimental results for Zoom applications, we can obtain more than 92.5% classification accuracy even if the captured image is intentionally edited to remove the traces of screen capture.
KW - Anti-forensics
KW - Image classification
KW - Online meeting system
KW - Screen capture
UR - http://www.scopus.com/inward/record.url?scp=85136950836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136950836&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-14463-9_16
DO - 10.1007/978-3-031-14463-9_16
M3 - Conference contribution
AN - SCOPUS:85136950836
SN - 9783031144622
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 244
EP - 255
BT - Machine Learning and Knowledge Extraction - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Proceedings
A2 - Holzinger, Andreas
A2 - Holzinger, Andreas
A2 - Holzinger, Andreas
A2 - Kieseberg, Peter
A2 - Tjoa, A Min
A2 - Weippl, Edgar
A2 - Weippl, Edgar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2022, held in conjunction with the 17th International Conference on Availability, Reliability and Security, ARES 2022
Y2 - 23 August 2022 through 26 August 2022
ER -