Farklı çözünürlükteki sayısal imge ve videolar için PRNU tabanlı kaynak kamera tespiti üzerine bir çalışma

Her bir kamera sensörüne has benzersiz bir gürültü bileşeni olan PRNU (Photo Response Non-Uniformity), sayısal imge ve videoların adli analizi kapsamında ihtiyaç duyulan önemli araçlardandır. PRNU’nun en yaygın uygulama alanı olan kaynak kamera tespiti, aynı marka ve model kameraların bile PRNU karakteristiğinin birbirinden farklı oluşu ve bu örüntünün çekilen her bir resim karesi üzerine bir kamera parmak izi gibi istemsiz eklenmesi esasına dayanmaktadır. Bir test dosyasından (imge ya da video) kestirimi sağlanan PRNU sensör gürültüsü ile dosyanın kaynağı olduğu düşünülen kameraya ait referans PRNU (sabit içerikli düz duvar ya da gökyüzü görüntülerinden yüksek doğrulukta elde edilen PRNU örüntüsü) arasındaki benzerliğe göre bu kameranın test videosunun kaynağı olup olamayacağı belirlenebilir. Sayısal video çerçevelerinin imgelere göre düşük kalitede kodlanması, videolardan kestirilen PRNU sensör gürültüsünün doğruluğunu, dolayısıyla da benzerlik analizini etkilemektedir. Bu bağlamda, videolarda PRNU tabanlı kaynak kamera tespitinde, referans PRNU’nun videolardansa imgelerden elde edilmesi performans etkinliği için önemli bir hamledir. Ancak, imge ve videolar aynı kaynak kamera ile çekilmiş olsalar dahi farklı en boy oranında ve/veya çözünürlükte kaydedilmektedirler. Bu sebeple, imgelerden elde edilen PRNU izinin, sorgu videosuna ait PRNU sensör gürültüsü ile aynı foto-alıcı hücrelere karşılık gelecek forma dönüştürülmesi gerekmektedir. Bu çalışmada, bu dönüşümü sağlayan ölçekleme ve kırpma parametrelerini hassas bir şekilde hesaplayabilen bir yöntem önerilmiştir.

Anahtar Kelimeler:

PRNU, Kaynak kamera tespiti, İmge ve videoların adli analizi

A study on PRNU-based source device identification for digital images and videos of different resolutions

PRNU (Photo Response Non-Uniformity), a noise pattern unique to each camera sensor, is one of the critical tools exploited for the forensic analysis of digital images and videos. Source camera attribution, the most widespread application of PRNU, is based on distinctive PRNU characteristics even of the same brand and model cameras and the inherent integration of this pattern into each exposed image or video frame as a camera fingerprint. It can be discovered whether a suspected camera may be the source of a query image or video based on a similarity test between the PRNU noise estimated from the query image/video and the reference PRNU of the camera that can be obtained accurately from a set of still-scene, e.g., wall or sky, images. In contrast to the images, low-quality encoding of digital video frames affects the accuracy of the estimated PRNU noise from video and hence of the similarity analysis. In this context, it may be wise to obtain the reference PRNU from images rather than videos for performance efficiency when working with source camera attribution for videos. However, images and videos are recorded in different aspect ratios and/or resolutions even though they are shot with the same source camera. Therefore, the reference PRNU obtained through images should be converted to the form corresponding to the same photosensitive cells as the query video PRNU noise. This paper proposes a technique to precisely estimate the scaling and cropping parameters leading to this geometric conversion.

Keywords:

PRNU, Source camera identification, Image and video forensics,

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J. Lukáš, J. Fridrich, and M. Goljan, Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur., 1 (2), 205–214, 2006. https://doi.org/10.1109/TIFS.2006.873602.
M. Chen, J. Fridrich, M. Goljan, and J. Lukas, Source digital camcorder identification using sensor photo response non-uniformity. Security, steganography, and watermarking of multimedia contents IX, 6505, 517-528, 2007. https://doi.org/10.1117/12.696519.
M. Chen, J. Fridrich, M. Goljan, and J. Lukás, Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur., 3 (1), 74–90, 2008. https://doi.org/10.1109/TIFS.2 007.916285.
T. Filler, J. Fridrich, and M. Goljan, Using sensor pattern noise for camera model identification. IEEE International Conference on Image Processing, pp. 1296–1299, San Diego, CA, USA, 2008.
J. Lukáš, J. Fridrich, and M. Goljan, Detecting digital image forgeries using sensor pattern noise. Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, pp. 362–372, San Jose, California, United States, 2006. https://doi.org/10.1117/12.640109.
X. Kang, Y. Li, Z. Qu, and J. Huang, Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur., 7 (2), 393–402, 2012. https://doi.org/10.1109/TIFS.2011.21682 14.
A. Lawgaly, F. Khelifi, and A. Bouridane, Weighted averaging-based sensor pattern noise estimation for source camera identification. IEEE International Conference on Image Processing (ICIP), pp. 5357–5361, Paris, France, 2014.
A. Karaküҫük, A. E. Dirik, H. T. Sencar, and N. D. Memon, Recent advances in counter PRNU based source attribution and beyond. Media Watermarking, Security, and Forensics, 9409, 201-211, 2015. https://doi.org/10.1117/12.21824 58.
A. Lawgaly and F. Khelifi, Sensor pattern noise estimation based on improved locally adaptive DCT filtering and weighted averaging for source camera identification and verification. IEEE Trans. Inf. Forensics Secur., 12 (2), 392–404, 2017. https://doi.org/10.1109/TIFS.2016.2620280.
F. Ahmed, F. Khelifi, A. Lawgaly, and A. Bouridane, Comparative analysis of a deep convolutional neural network for source camera identification. IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), pp. 1–6, London, UK, 2019.
A. Karaküçük and A. E. Dirik, PRNU based source camera attribution for image sets anonymized with patch-match algorithm. Digit. Investig., 30, 43–51, 2019. https://doi.org/10.1016/j.diin.2019.06. 001.
Y. Akbari, S. Al-maadeed, O. Elharrouss, F. Khelifi, A. Lawgaly, and A. Bouridane, Digital forensic analysis for source video identification: A survey. Forensic Sci. Int. Digit. Investig., 41, 1-13, 2022. https://doi.org/10.1016/j.fsidi.2022.301390.
N. Mondaini, R. Caldelli, a. Piva, M. Barni, and V. Cappellini, Detection of malevolent changes in digital video for forensic applications. Security, steganography, and watermarking of multimedia contents IX, 6505, 300-311, 2007. https://doi.org/10.1117/12.704924.
W. H. Chuang, H. Su, and M. Wu, Exploring compression effects for improved source camera identification using strongly compressed video. IEEE International Conference on Image Processing, pp. 1953–1956, Brussels, Belgium, 2011.
T. Höglund, P. Brolund, and K. Norell, Identifying camcorders using noise patterns from video clips recorded with image stabilisation. 7th International Symposium on Image and Signal Processing and Analysis, pp. 668–671, Dubrovnik, Croatia, 2011.
D.-K. Hyun, C.-H. Choi, and H.-K. Lee, Camcorder identification for heavily compressed low resolution videos. Comput. Sci. Converg. Lect. Notes Electr. Eng., 114, 695–791, 2012. https://doi.org/10.1007/978-94-007-2792-2_68.
A. Lawgaly, F. Khelifi, A. Bouridane, and S. Al-Maaddeed, Sensor pattern noise estimation using non-textured video frames for efficient source smartphone identification and verification. International Conference on Computing, Electronics & Communications Engineering (iCCECE), pp. 19–24, Southend, United Kingdom, 2021.
W.-C. Yang, J. Jiang, and C.-H. Chen, A fast source camera identification and verification method based on PRNU analysis for use in video forensic investigations. Multimed. Tools Appl., 80 (5), 6617–6638, 2021. https://doi.org/10.1007/s 11042-020-09763-z.
P. Ferrara, M. Iuliani, and A. Piva, PRNU-Based Video Source Attribution: Which Frames Are You Using?. Journal of Imaging, 8 (3), 1-14, 2022. https://doi.org/10.3390/jimaging8030057.
M. Goljan and J. Fridrich, Camera identification from cropped and scaled images. Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 6819, 154-166, 2008. https://doi.org/10.1117/12.766732.
D. Shullani, M. Fontani, M. Iuliani, O. Al Shaya, and A. Piva, VISION: a video and image dataset for source identification. EURASIP Journal on Information Security, 15, 1-16, 2017. https://doi.org/10.1186/s13635-017-0067-2.
M. Iuliani, M. Fontani, D. Shullani, and A. Piva, Hybrid reference-based Video Source Identification. Sensors, 19 (3), 1-19, 2019. https://doi.org/10.33 90/s19030649.
A. E. Dirik and A. Karaküçük, Forensic use of photo response non-uniformity of imaging sensors and a counter method. Opt. Express, 22 (1), 470–482, 2014. https://doi.org/10.1364/OE.22.000 470.
P. Ferrara and L. Beslay, Robust video source recognition in presence of motion stabilization. 8th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, Porto, Portugal, 2020.