research-article

PRNU-based Image Forgery Localization with Deep Multi-scale Fusion

Authors:

Mingfu XueAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 2

Article No.: 67, Pages 1 - 20

https://doi.org/10.1145/3548689

Published: 06 February 2023 Publication History

Abstract

Photo-response non-uniformity (PRNU), as a class of device fingerprint, plays a key role in the forgery detection/localization for visual media. The state-of-the-art PRNU-based forensics methods generally rely on the multi-scale trace analysis and result fusion, with Markov random field model. However, such hand-crafted strategies are difficult to provide satisfactory multi-scale decision, exhibiting a high false-positive rate. Motivated by this, we propose an end-to-end multi-scale decision fusion strategy, where a mapping from multi-scale forgery probabilities to binary decision is achieved by a supervised deep fully connected neural network. As the first time, the deep learning technology is employed in PRNU-based forensics for more flexible and reliable integration of multi-scale information. The benchmark experiments exhibit the state-of-the-art accuracy performance of our method in both pixel-level and image-level, especially for false positives. Additional robustness experiments also demonstrate the benefits of the proposed method in resisting noise and compression attacks.

References

[1]

Rainer Böhme, Matthias Kirchner, S. Katzenbeisser, and F. Petitcolas. 2016. Media forensics. In Information Hiding. Artech House, 231–259.

[2]

Xuanjing Shen, Zenan Shi, and Haipeng Chen. 2017. Splicing image forgery detection using textural features based on the grey level co-occurrence matrices. IET Image Process. 11, 1 (2017), 44–53.

[3]

Irene Amerini, Roberto Caldelli, Vito Cappellini, Francesco Picchioni, and Alessandro Piva. 2009. Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification. In Proceedings of the IEEE International Conference on Digital Signal Processing. 1–7.

[4]

Irene Amerini, Lamberto Ballan, Roberto Caldelli, Alberto Del Bimbo, and Giuseppe Serra. 2011. A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans. Inf. Forens. Secur. 6, 3 (2011), 1099–1110.

Digital Library

[5]

Anselmo Ferreira, Siovani C. Felipussi, Carlos Alfaro, Pablo Fonseca, John E. Vargas-Munoz, Jefersson A. Dos Santos, and Anderson Rocha. 2016. Behavior knowledge space-based fusion for copy–move forgery detection. IEEE Trans. Image Process. 25, 10 (2016), 4729–4742.

Digital Library

[6]

Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. 2015. Efficient dense-field copy–move forgery detection. IEEE Trans. Inf. Forens. Secur. 10, 11 (2015), 2284–2297.

Digital Library

[7]

Vincent Christlein, Christian Riess, Johannes Jordan, Corinna Riess, and Elli Angelopoulou. 2012. An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forens. Secur. 7, 6 (2012), 1841–1854.

Digital Library

[8]

Davide Cozzolino and Luisa Verdoliva. 2019. Noiseprint: A CNN-based camera model fingerprint. IEEE Trans. Inf. Forens. Secur. 15 (2019), 144–159.

[9]

Yijun Quan and Chang-Tsun Li. 2020. On addressing the impact of ISO speed upon PRNU and forgery detection. IEEE Trans. Inf. Forens. Secur. 16 (2020), 190–202.

Digital Library

[10]

Samet Taspinar, Manoranjan Mohanty, and Nasir Memon. 2020. Camera fingerprint extraction via spatial domain averaged frames. IEEE Trans. Inf. Forens. Secur. 15 (2020), 3270–3282.

[11]

Beijing Chen, Weijin Tan, Gouenou Coatrieux, Yuhui Zheng, and Yun Qing Shi. 2021. A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Trans. Multimedia 23 (2021), 3506–3517. DOI:

Digital Library

[12]

Peiyu Zhuang, Haodong Li, Shunquan Tan, Bin Li, and Jiwu Huang. 2021. Image tampering localization using a dense fully convolutional network. IEEE Trans. Inf. Forens. Secur. 16 (2021), 2986–2999.

[13]

Yang Wei, Zhuzhu Wang, Bin Xiao, Ximeng Liu, Zheng Yan, and Jianfeng Ma. 2020. Controlling neural learning network with multiple scales for image splicing forgery detection. ACM Trans. Multimed. Comput. Commun. Appl. 16, 4 (2020), 1–22.

Digital Library

[14]

Longyin Wen, Honggang Qi, and Siwei Lyu. 2018. Contrast enhancement estimation for digital image forensics. ACM Trans. Multimed. Comput. Commun. Appl. 14, 2 (2018), 1–21.

Digital Library

[15]

Priyanka Singh, Balasubramanian Raman, Nishant Agarwal, and Pradeep K. Atrey. 2017. Secure cloud-based image tampering detection and localization using POB number system. ACM Trans. Multimed. Comput. Commun. Appl. 13, 3 (2017), 1–23.

Digital Library

[16]

Ruchira Naskar and Rajat Subhra Chakraborty. 2013. A generalized tamper localization approach for reversible watermarking algorithms. ACM Trans. Multimed. Comput. Commun. Appl. 9, 3 (2013), 1–22.

Digital Library

[17]

Glenn E. Healey and Raghava Kondepudy. 1994. Radiometric CCD camera calibration and noise estimation. IEEE Trans. Pattern Anal. Mach. Intell. 16, 3 (1994), 267–276.

Digital Library

[18]

Jessica Fridrich. 2013. Sensor defects in digital image forensic. In Digital Image Forensics. Springer, 179–218.

[19]

Jan Lukas, Jessica Fridrich, and Miroslav Goljan. 2006. Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forens. Secur. 1, 2 (2006), 205–214.

Digital Library

[20]

Alan J. Cooper. 2013. Improved photo response non-uniformity (PRNU) based source camera identification. Forens. Sci. Int. 226, 1-3 (2013), 132–141.

[21]

Xiangui Kang, Jiansheng Chen, Kerui Lin, and Peng Anjie. 2014. A context-adaptive SPN predictor for trustworthy source camera identification. EURASIP J. Image Video Process. 2014, 1 (2014), 1–11.

[22]

Hui Zeng and Xiangui Kang. 2016. Fast source camera identification using content adaptive guided image filter. J. Forens. Sci. 61, 2 (2016), 520–526.

[23]

Hany Farid and Siwei Lyu. 2003. Higher-order wavelet statistics and their application to digital forensics. In Proceedings of the IEEE Computer Society Conference on Computer Vision Pattern Recognition Workshops, Vol. 8. 94–94.

[24]

Jinwei Wang, Ting Li, Yun-Qing Shi, Shiguo Lian, and Jingyu Ye. 2017. Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed. Tools Appl. 76, 22 (2017), 23721–23737.

Digital Library

[25]

Abhinav Gupta and Divya Singhal. 2018. Analytical global median filtering forensics based on moment histograms. ACM Trans. Multimed. Comput. Commun. Appl. 14, 2 (2018), 1–23.

Digital Library

[26]

Luisa Verdoliva, Davide Cozzolino, and Giovanni Poggi. 2014. A feature-based approach for image tampering detection and localization. In Proceedings of the IEEE International Workshop on Information Forensics Security. 149–154.

[27]

Haodong Li, Weiqi Luo, Xiaoqing Qiu, and Jiwu Huang. 2016. Identification of various image operations using residual-based features. IEEE Trans. Circ. Syst. Video Technol. 28, 1 (2016), 31–45.

Digital Library

[28]

Ashref Lawgaly, Fouad Khelifi, and Ahmed Bouridane. 2014. Weighted averaging-based sensor pattern noise estimation for source camera identification. In Proceedings of the IEEE International Conference on Image Processing. 5357–5361.

[29]

Xiangui Kang, Yinxiang Li, Zhenhua Qu, and Jiwu Huang. 2011. Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forens. Secur. 7, 2 (2011), 393–402.

Digital Library

[30]

Xufeng Lin and Chang-Tsun Li. 2015. Preprocessing reference sensor pattern noise via spectrum equalization. IEEE Trans. Inf. Forens. Secur. 11, 1 (2015), 126–140.

Digital Library

[31]

Jan Lukáš, Jessica Fridrich, and Miroslav Goljan. 2006. Detecting digital image forgeries using sensor pattern noise. In Security, Steganography, and Watermarking of Multimedia Contents VIII, Vol. 6072, Edward J. Delp III and Ping Wah Wong (Eds.). SPIE, 362–372. DOI:

[32]

Mo Chen, Jessica Fridrich, Miroslav Goljan, and Jan Lukás. 2008. Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forens. Secur. 3, 1 (2008), 74–90.

Digital Library

[33]

Giovanni Chierchia, Sara Parrilli, Giovanni Poggi, Luisa Verdoliva, and Carlo Sansone. 2011. PRNU-based detection of small-size image forgeries. In Proceedings of the IEEE International Conference on Digital Signal Processing. 1–6.

[34]

Giovanni Chierchia, Davide Cozzolino, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 2014. Guided filtering for PRNU-based localization of small-size image forgeries. In Proceedings of the IEEE International Conference on Acoustic Speech Signal Processing. 6231–6235.

[35]

Kaiming He, Jian Sun, and Xiaoou Tang. 2012. Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 6 (2012), 1397–1409.

Digital Library

[36]

Paweł Korus and Jiwu Huang. 2016. Multi-scale analysis strategies in PRNU-based tampering localization. IEEE Trans. Inf. Forens. Secur. 12, 4 (2016), 809–824.

Digital Library

[37]

Weiwei Zhang, Xinhua Tang, Zhenghong Yang, and Shaozhang Niu. 2019. Multi-scale segmentation strategies in PRNU-based image tampering localization. Multimed. Tools Appl. 78, 14 (2019), 20113–20132.

Digital Library

[38]

Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 8 (2007), 2080–2095.

[39]

C. Zhang and Philip C. Woodland. 2016. DNN speaker adaptation using parameterised sigmoid and ReLU hidden activation functions. In Proceedings of the IEEE International Conference on Acoustic Speech Signal Processing. 5300–5304.

Digital Library

[40]

Fatih Ertam and Galip Aydın. 2017. Data classification with deep learning using Tensorflow. In Proceedings of the IEEE International Conference on Computer Science and Engineering (UBMK). 755–758.

[41]

Dabal Pedamonti. 2018. Comparison of non-linear activation functions for deep neural networks on MNIST classification task. ArXiv Preprint ArXiv:1804.02763 (2018).

[42]

Sheng Lu, Feng Gao, Changhao Piao, and Ying Ma. 2019. Dynamic weighted cross entropy for semantic segmentation with extremely imbalanced data. In Proceedings of the IEEE International Conference on Artificial Intelligence and Advanced Manufacturing. 230–233. DOI:

[43]

Mina Jafari, Ruizhe Li, Yue Xing, Dorothee Auer, Susan Francis, Jonathan Garibaldi, and Xin Chen. 2019. FU-net: Multi-class image segmentation using feedback weighted U-net. In Lecture Notes in Computer Science. Springer, 529–537.

[44]

Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M. Jorge Cardoso. 2017. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Lecture Notes in Computer Science.Springer, 240–248.

[45]

Duc-Tien Dang-Nguyen, Cecilia Pasquini, Valentina Conotter, and Giulia Boato. 2015. Raise: A raw images dataset for digital image forensics. In Proceedings of the ACM Multimedia Systems Conference.219–224.

Digital Library

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Song YJiang WChai XGan ZZhou MChen L(2024)Cross-attention based two-branch networks for document image forgery localization in the MetaverseACM Transactions on Multimedia Computing, Communications, and Applications10.1145/368615821:2(1-24)Online publication date: 30-Dec-2024
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Bowen DHaiquan WYuxuan LZhao JMa YRunhe H(2024)Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on BlockchainACM Transactions on Sensor Networks10.1145/3673656Online publication date: 17-Jun-2024
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Li YYe LCao HWang WHua Z(2024)Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3669905Online publication date: 29-May-2024
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Index Terms

PRNU-based Image Forgery Localization with Deep Multi-scale Fusion
1. Applied computing
  1. Computer forensics
    1. Investigation techniques
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 2

March 2023

540 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572860

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 06 February 2023

Online AM: 14 July 2022

Accepted: 05 July 2022

Revised: 20 May 2022

Received: 19 January 2022

Published in TOMM Volume 19, Issue 2

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Funding Sources

Nanjing University of Aeronautics and Astronautics Graduate Research and Practice Innovation Program Project
National Natural Science Foundation of China
Guangxi Key Laboratory of Trusted Software
Basic Research Program of Jiangsu Province

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https://doi.org/10.1145/3686158
Bowen DHaiquan WYuxuan LZhao JMa YRunhe H(2024)Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on BlockchainACM Transactions on Sensor Networks10.1145/3673656Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673656
Li YYe LCao HWang WHua Z(2024)Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3669905Online publication date: 29-May-2024
https://doi.org/10.1145/3669905
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