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Blind Source Camera Identification of Online Social Network Images Using Adaptive Thresholding Technique

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1255)


The ubiquitous use of digital images has led to a new provocation in digital image forensics. Source camera identification authorizes forensic investigators to discover the probable source model that are appointed to acquire the image under investigation in criminal cases like terrorism, child pornography, etc. The techniques proposed till date, to identify the source camera, are inappropriate when applied to images captured with smartphones, subsequently uploaded and downloaded from these online social networks (OSN) such as Facebook, WhatsApp, etc. Such images lose their original quality, resolution, and statistical properties as online social platforms compress the images substantially, for efficient storage and transmission. Here, we present an efficient forensic image-source identification procedure, which follows an adaptive thresholding mechanism to differentiate between high and low image-camera correlation. The results of our experiments demonstrate that the proposed method drastically improves the performance of blind source camera identification of compressed OSN images as compared to traditional global threshold approach.


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  1. Sameer, V.U., Sugumaran, S., Naskar, R.: K-unknown models detection through clustering in blind source camera identification. IET Image Proc. 12(7), 1204–1213 (2018)

    Article  Google Scholar 

  2. Sameer, V.U., Sarkar, A., Naskar, R.: Source camera identification model: classifier learning, role of learning curves and their interpretation. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE (2017)

    Google Scholar 

  3. Gloe, T., Böhme, R.: The dresden image database for benchmarking digital image forensics. In: Proceedings of the 2010 ACM Symposium on Applied Computing. ACM (2010)

    Google Scholar 

  4. Sameer, V.U., Dali, I., Naskar, R.: A deep learning based digital forensic solution to blind source identification of Facebook images. In: International Conference on Information Systems Security. Springer, Cham (2018)

    Google Scholar 

  5. Gelogo, Y.E., Kim, T.H.: Compressed images transmission issues and solutions. Int. J. Comput. Graph. 5(1), 1–8 (2013)

    Google Scholar 

  6. Shullani, D., Fontani, M., Iuliani, M., Shaya, O.A., Piva, A.: Vision: a video and image dataset for source identification. EURASIP J. Inf. Secur. 2017(1), 15 (2017)

    Article  Google Scholar 

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Correspondence to Bhola Nath Sarkar .

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Sarkar, B.N., Barman, S., Naskar, R. (2021). Blind Source Camera Identification of Online Social Network Images Using Adaptive Thresholding Technique. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore.

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