Liu Liu
ABSTRACT
Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the <u>fi</u>ngerprint <u>t</u>o <u>s</u>oftware noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise.
- Mustafa Al-Ani and Fouad Khelifi. 2016. On the SPN estimation in image forensics: a systematic empirical evaluation. IEEE Transactions on Information Forensics and Security 12, 5 (2016), 1067--1081.Google Scholar
Digital Library
- Omar Al Shaya, Pengpeng Yang, Rongrong Ni, Yao Zhao, and Alessandro Piva. 2018. A new dataset for source identification of high dynamic range images. Sensors 18, 11 (2018), 3801.Google ScholarCross Ref
- Enes Altinisik and Hüsrev Taha Sencar. 2020. Source camera verification for strongly stabilized videos. IEEE Transactions on Information Forensics and Security 16 (2020), 643--657.Google ScholarCross Ref
- Enes Altinisik, Kasim Tasdemir, and Husrev Taha Sencar. 2019. Mitigation of H. 264 and H. 265 video compression for reliable PRNU estimation. IEEE Transactions on information forensics and security 15 (2019), 1557--1571.Google ScholarDigital Library
- E. Altinisik, K. Tasdemir, and H. T. Sencar. 2020. Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation. IEEE transactions on information forensics and security 15 (2020), 1557--1571.Google Scholar
- Irene Amerini, Roberto Caldelli, Andrea Del Mastio, Andrea Di Fuccia, Cristiano Molinari, and Anna Paola Rizzo. 2017. Dealing with video source identification in social networks. signal processing: Image communication 57 (2017), 1--7.Google Scholar
- Zhongjie Ba, Sixu Piao, Xinwen Fu, Dimitrios Koutsonikolas, Aziz Mohaisen, and Kui Ren. 2018. ABC: Enabling smartphone authentication with built-in camera. In 25th Annual Network and Distributed System Security Symposium, NDSS.Google ScholarCross Ref
- Zhongjie Ba, Zhan Qin, Xinwen Fu, and Kui Ren. 2019. CIM: Camera in Motion for Smartphone Authentication. IEEE Trans. Inf. Forensics Secur. 14, 11 (2019), 2987--3002. https://doi.org/10.1109/TIFS.2019.2911173Google ScholarDigital Library
- Zhongjie Ba, Xinyu Zhang, Zhan Qin, and Kui Ren. 2019. CFP: Enabling Camera Fingerprint Concealment for Privacy-Preserving Image Sharing. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 1094--1105.Google Scholar
- Sevinç Bayram, Hüsrev Taha Sencar, and Nasir Memon. 2012. Efficient sensor fingerprint matching through fingerprint binarization. IEEE Transactions on Information Forensics and Security 7, 4 (2012), 1404--1413.Google ScholarDigital Library
- Sevinc Bayram, Husrev Taha Sencar, and Nasir Memon. 2014. Sensor fingerprint identification through composite fingerprints and group testing. IEEE Transactions on information forensics and security 10, 3 (2014), 597--612.Google ScholarDigital Library
- Walter Bender, Daniel Gruhl, Norishige Morimoto, and Anthony Lu. 1996. Techniques for data hiding. IBM systems journal 35, 3.4 (1996), 313--336.Google Scholar
- Luca Bondi, Paolo Bestagini, Fernando Perez-Gonzalez, and Stefano Tubaro. 2018. Improving PRNU compression through preprocessing, quantization, and coding. IEEE Transactions on Information Forensics and Security 14, 3 (2018), 608--620.Google ScholarCross Ref
- Mo Chen, Jessica Fridrich, and Miroslav Goljan. 2007. Digital imaging sensor identification (further study). In Security, steganography, and watermarking of multimedia contents IX, Vol. 6505. SPIE, 258--270.Google Scholar
- Mo Chen, Jessica Fridrich, Miroslav Goljan, and Jan Lukás. 2008. Determining image origin and integrity using sensor noise. IEEE Transactions on information forensics and security 3, 1 (2008), 74--90.Google ScholarDigital Library
- Shaxun Chen, Amit Pande, Kai Zeng, and Prasant Mohapatra. 2014. Live video forensics: Source identification in lossy wireless networks. IEEE Transactions on Information Forensics and Security 10, 1 (2014), 28--39.Google ScholarCross Ref
- Yushi Cheng, Xiaoyu Ji, Juchuan Zhang, Wenyuan Xu, and Yi-Chao Chen. 2019. Demicpu: Device fingerprinting with magnetic signals radiated by cpu. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 1149--1170.Google ScholarDigital Library
- Giovanni Chierchia, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 2014. A Bayesian-MRF approach for PRNU-based image forgery detection. IEEE Transactions on Information Forensics and Security 9, 4 (2014), 554--567.Google ScholarDigital Library
- Morteza Darvish Morshedi Hosseini and Miroslav Goljan. 2019. Camera identification from HDR images. In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. 69--76.Google ScholarDigital Library
- Anupam Das, Nikita Borisov, and Matthew Caesar. 2014. Do you hear what I hear? Fingerprinting smart devices through embedded acoustic components. In Proceedings of the 2014 ACMSIGSAC Conference on Computer and Communications Security. 441--452.Google Scholar
- Anupam Das, Nikita Borisov, and Matthew Caesar. 2016. Tracking Mobile Web Users Through Motion Sensors: Attacks and Defenses.. In NDSS.Google Scholar
- Sanorita Dey, Nirupam Roy, Wenyuan Xu, Romit Roy Choudhury, and Srihari Nelakuditi. 2014. AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable.. In NDSS, Vol. 14. Citeseer, 23--26.Google ScholarCross Ref
- Marco Fanfani, Alessandro Piva, and Carlo Colombo. 2022. PRNU registration under scale and rotation transform based on Convolutional Neural Networks. Pattern Recognition 124 (2022), 108413.Google ScholarDigital Library
- Miroslav Goljan. 2008. Digital camera identification from images-estimating false acceptance probability. In International workshop on digital watermarking. Springer, 454--468.Google Scholar
- Miroslav Goljan and Jessica Fridrich. 2008. Camera identification from cropped and scaled images. In Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, Vol. 6819. SPIE, 154--166.Google Scholar
- Miroslav Goljan, Jessica Fridrich, and Mo Chen. 2010. Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Transactions on Information Forensics and Security 6, 1 (2010), 227--236.Google ScholarDigital Library
- Miroslav Goljan, Jessica Fridrich, and Tomá? Filler. 2009. Large scale test of sensor fingerprint camera identification. In Media forensics and security, Vol. 7254. International Society for Optics and Photonics, 72540I.Google Scholar
- C Holt. 1987. Two-channel likelihood detectors for arbitrary linear channel distortion. IEEE Transactions on Acoustics, Speech, and Signal Processing 35, 3 (1987), 267--273.Google ScholarCross Ref
- Massimo Iuliani, Marco Fontani, Dasara Shullani, and Alessandro Piva. 2019. Hybrid reference-based video source identification. Sensors 19, 3 (2019), 649.Google ScholarCross Ref
- Xiangui Kang, Yinxiang Li, Zhenhua Qu, and Jiwu Huang. 2011. Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Transactions on Information Forensics and Security 7, 2 (2011), 393--402.Google ScholarDigital Library
- AK Karunakar, Chang-Tsun Li, et al. 2021. Identification of source social network of digital images using deep neural network. Pattern Recognition Letters 150 (2021), 17--25.Google ScholarDigital Library
- Blossom Kaur, Amandeep Kaur, and Jasdeep Singh. 2011. Steganographic approach for hiding image in DCT domain. International Journal of Advances in Engineering & Technology 1, 3 (2011), 72.Google Scholar
- Marissa Koopman, Andrea Macarulla Rodriguez, and Zeno Geradts. 2018. Detection of deepfake video manipulation. In The 20th Irish machine vision and image processing conference (IMVIP). 133--136.Google Scholar
- Paweŀ Korus and Jiwu Huang. 2016. Multi-scale analysis strategies in PRNUbased tampering localization. IEEE Transactions on Information Forensics and Security 12, 4 (2016), 809--824.Google ScholarDigital Library
- Tomer Laor, Naif Mehanna, Antonin Durey, Vitaly Dyadyuk, Pierre Laperdrix, Clémentine Maurice, Yossi Oren, Romain Rouvoy, Walter Rudametkin, and Yuval Yarom. 2022. DRAWNAPART: A Device Identification Technique based on Remote GPU Fingerprinting. arXiv preprint arXiv:2201.09956 (2022).Google Scholar
- Ashref Lawgaly and Fouad Khelifi. 2016. Sensor pattern noise estimation based on improved locally adaptive DCT filtering and weighted averaging for source camera identification and verification. IEEE Transactions on Information Forensics and Security 12, 2 (2016), 392--404.Google ScholarDigital Library
- Chang-Tsun Li. 2010. Source camera identification using enhanced sensor pattern noise. IEEE Transactions on Information Forensics and Security 5, 2 (2010), 280--287.Google ScholarDigital Library
- Chang-Tsun Li and Yue Li. 2011. Color-decoupled photo response non-uniformity for digital image forensics. IEEE Transactions on Circuits and Systems for Video Technology 22, 2 (2011), 260--271.Google ScholarDigital Library
- Jian Li, Yang Lv, Bin Ma, Meihong Yang, Chunpeng Wang, and Yang Zheng. 2020. Video source identification algorithm based on 3D geometric transformation. Computer Systems Science and Engineering 35, 6 (2020), 513--521.Google ScholarCross Ref
- Ruizhe Li, Chang-Tsun Li, and Yu Guan. 2018. Inference of a compact representation of sensor fingerprint for source camera identification. Pattern Recognition 74 (2018), 556--567.Google ScholarDigital Library
- Xufeng Lin and Chang-Tsun Li. 2015. Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Transactions on Information Forensics and Security 11, 1 (2015), 126--140.Google ScholarDigital Library
- Liu Liu, Hanlin Yu, Zhongjie Ba, Li Lu, Feng Lin, and Kui Ren. 2021. PassFace: Enabling Practical Anti-Spoofing Facial Recognition with Camera Fingerprinting. In ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, June 14-23, 2021. IEEE, 1--6. https://doi.org/10.1109/ICC42927.2021. 9501053Google ScholarCross Ref
- Raquel Ramos López, Ana Lucila Sandoval Orozco, and Luis Javier García Villalba. 2021. Compression effects and scene details on the source camera identification of digital videos. Expert Systems with Applications 170 (2021), 114515.Google ScholarCross Ref
- Jan Lukas, Jessica Fridrich, and Miroslav Goljan. 2006. Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1, 2 (2006), 205--214.Google ScholarDigital Library
- Bin Ma, Yuanyuan Hu, Jian Li, ChunpengWang, Meihong Yang, and Yang Zheng. 2021. PRNU Extraction from Stabilized Video: A Patch Maybe Better than a Bunch. COMPUTER SYSTEMS SCIENCE AND ENGINEERING 36, 1 (2021), 189--200.Google ScholarCross Ref
- Sara Mandelli, Paolo Bestagini, Luisa Verdoliva, and Stefano Tubaro. 2019. Facing device attribution problem for stabilized video sequences. IEEE Transactions on Information Forensics and Security 15 (2019), 14--27.Google ScholarCross Ref
- Sara Mandelli, Davide Cozzolino, Paolo Bestagini, Luisa Verdoliva, and Stefano Tubaro. 2020. CNN-based fast source device identification. IEEE Signal Processing Letters 27 (2020), 1285--1289.Google ScholarCross Ref
- Francesco Marra, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 2017. Blind PRNU-based image clustering for source identification. IEEE Transactions on Information Forensics and Security 12, 9 (2017), 2197--2211.Google ScholarDigital Library
- Forensic Pathways. 2022. New forensic image technology achieves success in major child abuse case. https://www.forensic-pathways.com/new-forensicimage- technology-achieves-success-in-major-child-abuse-case/Google Scholar
- Yijun Quan and Chang-Tsun Li. 2020. On addressing the impact of ISO speed upon PRNU and forgery detection. IEEE Transactions on Information Forensics and Security 16 (2020), 190--202.Google ScholarDigital Library
- Erwin Quiring and Matthias Kirchner. 2015. Fragile sensor fingerprint camera identification. In 2015 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 1--6.Google ScholarCross Ref
- R Satta, J Galbally, and L Beslay. 2013. State-of-theart review: Video analytics for fight against on-line child abuse. Technical Report. Technical Report JRC85864, European Commission-Joint Research Centre.Google Scholar
- Samet Taspinar, Manoranjan Mohanty, and Nasir Memon. 2020. Camera identification of multi-format devices. Pattern Recognition Letters 140 (2020), 288--294.Google ScholarDigital Library
- Diego Valsesia, Giulio Coluccia, Tiziano Bianchi, and Enrico Magli. 2015. Compressed fingerprint matching and camera identification via random projections. IEEE Transactions on Information Forensics and Security 10, 7 (2015), 1472--1485.Google ScholarDigital Library
- Diego Valsesia, Giulio Coluccia, Tiziano Bianchi, and Enrico Magli. 2017. User authentication via PRNU-based physical unclonable functions. IEEE Transactions on Information Forensics and Security 12, 8 (2017), 1941--1956.Google ScholarDigital Library
- Yue Zheng, Yuan Cao, and Chip-Hong Chang. 2019. APUF-based data-device hash for tampered image detection and source camera identification. IEEE Transactions on information forensics and security 15 (2019), 620--634.Google ScholarDigital Library
- Xinyan Zhou, Xiaoyu Ji, Chen Yan, Jiangyi Deng, and Wenyuan Xu. 2019. Nauth: Secure face-to-face device authentication via nonlinearity. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2080--2088.Google ScholarDigital Library
- Zhe Zhou, Wenrui Diao, Xiangyu Liu, and Kehuan Zhang. 2014. Acoustic fingerprinting revisited: Generate stable device id stealthily with inaudible sound. In Proceedings of the 2014 ACMSIGSAC Conference on Computer and Communications Security. 429--440.Google ScholarDigital Library
Index Terms
- FITS: Matching Camera Fingerprints Subject to Software Noise Pollution
Recommendations
Latent Fingerprint Matching
Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and ...
Building fingerprints with information from three color bands for source camera identification
MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligenceThe imaging sensor (e.g., CCD, CMOS) pattern noise is a noise-like spread-spectrum signal that mainly consists of the photo-response non-uniformity (PRNU) noise. Since each camera has a unique PRNU, the PRNU becomes a popular fingerprint for source ...
A robust fingerprint matching method
This paper is concerned with accurate and efficient fingerprint matching. We have two main contributions:(1)define a novel feature vector for each fingerprint minutia based on the global orientation field. These features are used to identify ...
Comments