Abstract
This paper presents our research work in camera verification. We expanded a convolutional network-based feature extraction/verification network to a multi-patch input and addressed the concerns over memory limitation and overfitting issue. We have also made careful consideration for custom model training and provided strong results showing promising potential for real-world application of detecting scene text repurposing.
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References
Akshatha, K.R., Karunakar, A.K., Anitha, H., Raghavendra, U., Shetty, D.: Digital camera identification using PRNU: a feature based approach. Digit. Investig. 19, 69–77 (2016). ISSN 1742–2876. https://doi.org/10.1016/j.diin.2016.10.002. https://www.sciencedirect.com/science/article/pii/S1742287616300998
Goljan, J.F., Chen, M.: Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Trans. Inf. Secur. Forensics 6(1), 227–236 (2010)
Singh, R., Vatsa, M., Noore, A.: Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model. Signal Process. 87(11), 2746–2764 (2007)
Banerjee, S., Ross, A.: Impact of photometric transformations on PRNU estimation schemes: a case study using near infrared ocular images. In: 2018 International Workshop on Biometrics and Forensics (IWBF), Sassari, Italy, pp. 1–8 (2018). https://doi.org/10.1109/IWBF.2018.8401560
Cozzolino, D., Verdoliva, L.: Noiseprint: a CNN-based camera model fingerprint, CoRR, abs/1808.08396 (2018)
Athanasiadou, E., Geradts, Z., Van Eijk, E.: Camera recognition with deep learning. Forensic Sci. Res. 3(3), 210–218 (2018). https://doi.org/10.1080/20961790.2018.1485198
Cozzolino, D., Marra, F., Gragnaniello, D., Poggi, G., Verdoliva, L.: Combining PRNU and noiseprint for robust and efficient device source identification. EURASIP J. Inf. Secur. 2020(1), 1–12 (2020). https://doi.org/10.1186/s13635-020-0101-7
Fiscus, J., et al.: MediFor Challenge Evaluation Overview, p. 163 (2020). https://mig.nist.gov/MFC/Web/PIMeeting2020/NIST_MFC20_PIMeeting_All_Final_formated.pdf
. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015
Bayar, B., Stamm, M.C.:Towards open set camera model identification using a deep learning framework. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, pp. 2007–2011 (2018). https://doi.org/10.1109/ICASSP.2018.8462383
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Cao, H., AbdAlmageed, W. (2021). Customizable Camera Verification for Media Forensic. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_24
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DOI: https://doi.org/10.1007/978-3-030-86334-0_24
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