Abstract
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.
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References
Agarwal, S., Fan, W., Farid, H.: A diverse large-scale dataset for evaluating rebroadcast attacks. In: Proceedings of ICASSP 2018, pp. 1997–2001 (2018)
Duda, R.O., Hart, P.E., Stork, D.G., et al.: Pattern Classification. Wiley, New York (1973)
Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)
He, M.: Distinguish computer generated and digital images: a CNN solution. Concurr. Comput. Pract. Exp. 31, e4788 (2018)
He, P., Jiang, X., Sun, T., Li, H.: Computer graphics identification combining convolutional and recurrent neural networks. IEEE Signal Process. Lett. 25, 1369–1373 (2018)
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282 (1995)
Hsu, Y.F., Chang, S.F.: Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans. Inf. Forensics Secur. 5(4), 816–825 (2010)
Huang, F., Huang, J., Shi, Y.Q.: Detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forensics Secur. 5(4), 848–856 (2010)
Li, H., Wang, S., Kot, A.C.: Image recapture detection with convolutional and recurrent neural networks. Electron. Imaging Med. Watermark. Secur. Forensics 5, 87–91 (2017)
Lukáš, J., Fridrich, J.: Estimation of primary quantization matrix in double compressed JPEG images. In: Proceedings of Digital Forensic Research Workshop, pp. 5–8 (2003)
Lukáš, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)
Pevný, T., Fridrich, J.: Detection of double-compression in JPEG images for applications in steganography. IEEE Trans. Inf. Forensics Secur. 3(2), 247–258 (2008)
Piva, A.: An overview on image forensics. ISRN Signal Process. 2013, 1–22 (2013)
Quan, W., Wan, K., Yan, D.M., Zhang, X.: Distinguishing between natural and computer-generated images using convolutional neural networks. IEEE Trans. Inf. Forensics Secur. 18, 2772–2787 (2018)
Rahmouni, N., Nozick, V., Yamagishi, J., Echizen, I.: Distinguishing computer graphics from natural images using convolution neural networks. In: Proceedings of WIFS 2017, pp. 1–6 (2017)
Stamm, M.C., Wu, M., Liu, K.J.R.: Information forensics: an overview of the first decade. IEEE Access 1, 167–200 (2013)
Yang, P., Ni, R., Zhao, Y.: Recapture image forensics based on Laplacian convolutional neural networks. In: Shi, Y.Q., Kim, H.J., Perez-Gonzalez, F., Liu, F. (eds.) IWDW 2016. LNCS, vol. 10082, pp. 119–128. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53465-7_9
Yu, I.J., Kim, D.G., Park, J.S., Hou, J.U., Choi, S., Lee, H.K.: Identifying photorealistic computer graphics using convolutional neural networks. In: Proceedings of ICIP 2017, pp. 4093–4097 (2017)
Acknowledgment
This research was supported by JSPS KAKENHI Grant Number 19K22846, JST SICORP Grant Number JPMJSC20C3, and JST CREST Grant Number JPMJCR20D3, Japan.
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Kuribayashi, M., Kamakari, K., Funabiki, N. (2022). Classification of Screenshot Image Captured in Online Meeting System. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2022. Lecture Notes in Computer Science, vol 13480. Springer, Cham. https://doi.org/10.1007/978-3-031-14463-9_16
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DOI: https://doi.org/10.1007/978-3-031-14463-9_16
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