Handbook of Big Data and IoT Security pp 211-223 | Cite as
Internet of Things Camera Identification Algorithm Based on Sensor Pattern Noise Using Color Filter Array and Wavelet Transform
- 2 Citations
- 1.8k Downloads
Abstract
The Internet of Things (IoT) is cutting-edge technology of recent decade and has influenced all aspects of our modern life. Its significance and wide-range applications necessitate imposing security and forensics techniques on IoT to obtain more reliability. Digital cameras are the noteworthy part of IoT that play a vital role in the variety of usages and this entails proposing forensic solutions to protect IoT and mitigate misapplication.
Identifying source camera of an image is an imperative subject in digital forensics. Noise characteristics of image, extraction of Sensor Pattern Noise (SPN) and its correlation with Photo Response Non-Uniformity (PRNU) has been employed in the majority of previously proposed methods. In this paper, a feature extraction method based on PRNU is proposed which provides features for classification with Support Vector Machine (SVM). The proposed method endeavours to separate more powerful signals which is linked to camera sensor pattern noise by identifying color filter array pattern. To overcome the computational complexity, the proposed method is boosted by utilizing wavelet transform plus reducing dimensions of the image by selecting the most important components of noise. Our experiments demonstrate that the proposed method outperforms in terms of accuracy and runtime.
Keywords
Internet of Things Forensic Camera identification Photo response non-uniformity (PRNU) Sensor pattern noise (SPN) Color filter array (CFA) Wavelet transform Support vector machine (SVM)References
- 1.M. Conti, A. Dehghantanha, K. Franke, S. Watson, Internet of things security and forensics: Challenges and opportunities, Future Generation Computer Systems 78 (2018) 544–546.CrossRefGoogle Scholar
- 2.S. Watson, A. Dehghantanha, Digital forensics: the missing piece of the internet of things promise, Computer Fraud & Security 2016 (2016) 5–8.CrossRefGoogle Scholar
- 3.S. Walker-Roberts, M. Hammoudeh, A. Dehghantanha, A systematic review of the availability and efficacy of countermeasures to internal threats in healthcare critical infrastructure, IEEE Access 6 (2018) 25167–25177.CrossRefGoogle Scholar
- 4.S. Zhongfu, Y. S. Du Keming, Development trend of internet of things and perspective of its application in agriculture [j], Agriculture Network Information 5 (2010) 21.Google Scholar
- 5.A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of things for smart cities, IEEE Internet of Things journal 1 (2014) 22–32.CrossRefGoogle Scholar
- 6.A. Azmoodeh, A. Dehghantanha, K. K. R. Choo, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning, IEEE Transactions on Sustainable Computing (2018) 1–1.Google Scholar
- 7.G. Epiphaniou, P. Karadimas, D. K. B. Ismail, H. Al-Khateeb, A. Dehghantanha, K. K. R. Choo, Non-reciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social IoT networks, IEEE Internet of Things Journal (2017) 1–1.Google Scholar
- 8.H. HaddadPajouh, A. Dehghantanha, R. Khayami, K.-K. R. Choo, A deep recurrent neural network based approach for internet of things malware threat hunting, Future Generation Computer Systems 85 (2018) 88–96.CrossRefGoogle Scholar
- 9.A. Azmoodeh, A. Dehghantanha, M. Conti, K.-K. R. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint, Journal of Ambient Intelligence and Humanized Computing (2018) 1–12.Google Scholar
- 10.H. H. Pajouh, R. Javidan, R. Khayami, D. Ali, K. K. R. Choo, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks, IEEE Transactions on Emerging Topics in Computing (2016) 1–1.Google Scholar
- 11.S. Homayoun, M. Ahmadzadeh, S. Hashemi, A. Dehghantanha, R. Khayami, Botshark: A deep learning approach for botnet traffic detection, Cyber Threat Intelligence (2018) 137–153.Google Scholar
- 12.O. Osanaiye, H. Cai, K.-K. R. Choo, A. Dehghantanha, Z. Xu, M. Dlodlo, Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing, EURASIP Journal on Wireless Communications and Networking 2016 (2016) 130.CrossRefGoogle Scholar
- 13.R. Hegarty, D. J. Lamb, A. Attwood, Digital evidence challenges in the internet of things., in: INC, pp. 163–172.Google Scholar
- 14.C. Esposito, A. Castiglione, F. Pop, K. K. R. Choo, Challenges of connecting edge and cloud computing: A security and forensic perspective, IEEE Cloud Computing 4 (2017) 13–17.CrossRefGoogle Scholar
- 15.A. Ariffin, K.-K. Choo, Z. Yunos, Forensic readiness: A case study on digital CCTV systems antiforensics, in: Contemporary Digital Forensic Investigations of Cloud and Mobile Applications, Elsevier, 2017, pp. 147–162.Google Scholar
- 16.J. Nakamura, Image sensors and signal processing for digital still cameras, CRC press, 2017.Google Scholar
- 17.J. Fridrich, Digital image forensics, IEEE Signal Processing Magazine 26 (2009) 26–37.CrossRefGoogle Scholar
- 18.T. V. Lanh, K. S. Chong, S. Emmanuel, M. S. Kankanhalli, A survey on digital camera image forensic methods, in: 2007 IEEE International Conference on Multimedia and Expo, pp. 16–19.Google Scholar
- 19.J. Lukas, J. Fridrich, M. Goljan, Digital camera identification from sensor pattern noise, IEEE Transactions on Information Forensics and Security 1 (2006) 205–214.CrossRefGoogle Scholar
- 20.C.-T. Li, Source camera linking using enhanced sensor pattern noise extracted from images (2009).Google Scholar
- 21.K. Matsushita, H. Kitazawa, An improved camera identification method based on the texture complexity and the image restoration, in: Proceedings of the 2009 International Conference on Hybrid Information Technology, ACM, pp. 171–175.Google Scholar
- 22.C.-T. Li, Y. Li, Digital camera identification using colour-decoupled photo response non-uniformity noise pattern, in: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, IEEE, pp. 3052–3055.Google Scholar
- 23.C. T. Li, Source camera identification using enhanced sensor pattern noise, IEEE Transactions on Information Forensics and Security 5 (2010) 280–287.CrossRefGoogle Scholar
- 24.X. Kang, Y. Li, Z. Qu, J. Huang, Enhancing source camera identification performance with a camera reference phase sensor pattern noise, IEEE Transactions on Information Forensics and Security 7 (2012) 393–402.CrossRefGoogle Scholar
- 25.E. Quiring, M. Kirchner, Fragile sensor fingerprint camera identification, in: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6.Google Scholar
- 26.D. Valsesia, G. Coluccia, T. Bianchi, E. Magli, User authentication via PRNU-based physical unclonable functions, IEEE Transactions on Information Forensics and Security 12 (2017) 1941–1956.CrossRefGoogle Scholar
- 27.V. U. Sameer, R. Naskar, N. Musthyala, K. Kokkalla, Deep learning based counter–forensic image classification for camera model identification, in: C. Kraetzer, Y.-Q. Shi, J. Dittmann, H. J. Kim (Eds.), Digital Forensics and Watermarking, Springer International Publishing, Cham, 2017, pp. 52–64.CrossRefGoogle Scholar
- 28.H. M. X. L. Abbas El Gamal, Boyd A. Fowler, Modeling and estimation of FPN components in CMOS image sensors, volume 3301, pp. 3301 – 3301 – 10.Google Scholar
- 29.A. Bosco, M. Mancuso, Noise filter for Bayer pattern image data, 2008. US Patent 7,369,165.Google Scholar
- 30.M. K. Mihcak, I. Kozintsev, K. Ramchandran, Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising, in: Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on, volume 6, IEEE, pp. 3253–3256.Google Scholar
- 31.C.-H. Choi, J.-H. Choi, H.-K. Lee, CFA pattern identification of digital cameras using intermediate value counting, in: Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, MM&Sec ’11, ACM, New York, NY, USA, 2011, pp. 21–26.Google Scholar
- 32.S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE transactions on pattern analysis and machine intelligence 11 (1989) 674–693.CrossRefGoogle Scholar
- 33.M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, B. Scholkopf, Support vector machines, IEEE Intelligent Systems and their applications 13 (1998) 18–28.CrossRefGoogle Scholar
- 34.S. Wold, K. Esbensen, P. Geladi, Principal component analysis, Chemometrics and intelligent laboratory systems 2 (1987) 37–52.CrossRefGoogle Scholar