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.
- Adaptive threshold
- Digital forensics
- Peak–to–correlation–energy ratio
- Sensor pattern noise
- Source camera identification
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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. https://doi.org/10.1007/978-981-15-7834-2_59
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7833-5
Online ISBN: 978-981-15-7834-2