research-article

Detection of Recolored Image by Texture Features in Chrominance Components

Authors:

Zhongyun HuaAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 3

Article No.: 121, Pages 1 - 23

https://doi.org/10.1145/3571076

Published: 25 February 2023 Publication History

Abstract

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values without altering the original image content. With the rapid proliferation of social network and image editing techniques, recolored images (RIs) have raised new security issues in society. Existing detection methods have good performance in detecting RIs for certain categories of recoloring techniques. However, the performance on the handcrafted recoloring scenario is still poor due to the influence of human prior knowledge. To deal with this problem, we explore a solution from the perspective of chrominance texture artifacts to improve the generalization ability. The results of the analysis show that natural images (NIs) and RIs have textural disparities in different color components, especially in the chrominance components (i.e., Cb, Cr, and H). Based on such new prior knowledge of statistical discriminability, we propose a feature set to capture texture features in chrominance components for identifying RIs. Extensive experimental results show that the proposed method can accurately identify RIs with certain categories of recoloring techniques, and outperforms existing methods in the scenario of handcrafted recoloring.

References

[1]

Luisa Verdoliva. 2020. Media forensics and deepfakes: An overview. IEEE J. Sel. Topics Signal Process. 14, 5 (2020), 910–932.

[2]

Yang Yu, Rongrong Ni, Wenjie Li, and Yao Zhao. 2022. Detection of AI-manipulated fake faces via mining generalized features. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (Mar.2022), Article 94, 23 pages. DOI:

Digital Library

[3]

Beijing Chen, Weijin Tan, Gouenou Coatrieux, Yuhui Zheng, and Yun-Qing Shi. 2020. A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE Trans. Multimedia 23 (2020), 3506–3517.

Digital Library

[4]

Haiwei Wu, Jiantao Zhou, and Yuanman Li. 2021. Deep generative model for image inpainting with local binary pattern learning and spatial attention. IEEE Trans. Multimedia 24 (2021), 4016–4027.

[5]

Hongyi Sun, Wanhua Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. 2022. Learning adaptive patch generators for mask-robust image inpainting. IEEE Trans. Multimedia (2022).

[6]

Ashwin Swaminathan, Min Wu, and K. J. Ray Liu. 2008. Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur. 3, 1 (2008), 101–117.

Digital Library

[7]

Longyin Wen, Honggang Qi, and Siwei Lyu. 2018. Contrast enhancement estimation for digital image forensics. ACM Trans. Multimedia Comput. Commun. Appl. 14, 2 (May2018), Article 49, 21 pages. DOI:

Digital Library

[8]

Ruyong Ren, Shaozhang Niu, Hua Ren, Shubin Zhang, Tengyue Han, and Xiaohai Tong. 2022. ESRNet: Efficient search and recognition network for image manipulation detection. ACM Trans. Multimedia Comput. Commun. Appl. 18, 4 (Mar.2022), Article 111, 23 pages. DOI:

Digital Library

[9]

Ido Yerushalmy and Hagit Hel-Or. 2011. Digital image forgery detection based on lens and sensor aberration. Int. J. Comput. Vision 92, 1 (2011), 71–91.

Digital Library

[10]

Zhigang Fan and Ricardo L. De Queiroz. 2003. Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans. Image Process. 12, 2 (2003), 230–235.

Digital Library

[11]

Weiqi Luo, Zhenhua Qu, Jiwu Huang, and Guoping Qiu. 2007. A novel method for detecting cropped and recompressed image block. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 2. II–217.

[12]

Yanyang Yan, Wenqi Ren, and Xiaochun Cao. 2018. Recolored image detection via a deep discriminative model. IEEE Trans. Inf. Forensics Secur. 14, 1 (2018), 5–17.

[13]

Erik Reinhard, Michael Adhikhmin, Bruce Gooch, and Peter Shirley. 2001. Color transfer between images. IEEE Comput. Graph. Appl. 21, 5 (2001), 34–41.

Digital Library

[14]

Francois Pitie, Anil C. Kokaram, and Rozenn Dahyot. 2005. N-dimensional probability density function transfer and its application to color transfer. In 10th IEEE International Conference on Computer Vision (ICCV’05), Vol. 2. 1434–1439.

Digital Library

[15]

Mingming He, Jing Liao, Dongdong Chen, Lu Yuan, and Pedro V. Sander. 2019. Progressive color transfer with dense semantic correspondences. ACM Trans. Graph. (TOG) 38, 2 (2019), 1–18.

Digital Library

[16]

Junyong Lee, Hyeongseok Son, Gunhee Lee, Jonghyeop Lee, Sunghyun Cho, and Seungyong Lee. 2020. Deep color transfer using histogram analogy. The Visual Comput. 36, 10 (2020), 2129–2143.

Digital Library

[17]

Yu-Wing Tai, Jiaya Jia, and Chi-Keung Tang. 2005. Local color transfer via probabilistic segmentation by expectation-maximization. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. 747–754.

[18]

Anat Levin, Dani Lischinski, and Yair Weiss. 2004. Colorization using optimization. ACM Trans. Graph. (TOG) 23, 3 (2004), 689–694.

Digital Library

[19]

Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. 2006. Manga colorization. ACM Trans. Graph. (TOG) 25, 3 (2006), 1214–1220.

Digital Library

[20]

Xiaobo An and Fabio Pellacini. 2008. AppProp: All-pairs appearance-space edit propagation. ACM Trans. Graph. (TOG) 27, 3 (2008), 1–9.

Digital Library

[21]

Kun Xu, Yong Li, Tao Ju, Shi-Min Hu, and Tian-Qiang Liu. 2009. Efficient affinity-based edit propagation using kd tree. ACM Trans. Graph. (TOG) 28, 5 (2009), 1–6.

Digital Library

[22]

Xiaowu Chen, Dongqing Zou, Jianwei Li, Xiaochun Cao, Qinping Zhao, and Hao Zhang. 2014. Sparse dictionary learning for edit propagation of high-resolution images. In Proceedings of the IEEE Conference on Computer Vision Pattern Recognition. (CVPR’14). 2854–2861.

Digital Library

[23]

Yuki Endo, Satoshi Iizuka, Yoshihiro Kanamori, and Jun Mitani. 2016. DeepProp: Extracting deep features from a single image for edit propagation. In Comput. Graph. Forum. 189–201.

[24]

Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, and Alexei A. Efros. 2017. Real-time user-guided image colorization with learned deep priors. arXiv:1705.02999. https://arxiv.org/abs/1705.02999.

[25]

Peter O’Donovan, Aseem Agarwala, and Aaron Hertzmann. 2011. Color compatibility from large datasets. ACM Trans. Graph. (TOG) 30, 4 (2011), 1–12.

Digital Library

[26]

Sharon Lin, Daniel Ritchie, Matthew Fisher, and Pat Hanrahan. 2013. Probabilistic color-by-numbers: Suggesting pattern colorizations using factor graphs. ACM Trans. Graph. (TOG) 32, 4 (2013), 1–12.

Digital Library

[27]

Huiwen Chang, Ohad Fried, Yiming Liu, Stephen DiVerdi, and Adam Finkelstein. 2015. Palette-based photo recoloring. ACM Trans. Graph. (TOG) 34, 4 (2015), 139–1.

Digital Library

[28]

Junho Cho, Sangdoo Yun, Kyoung Mu Lee, and Jin Young Choi. 2017. Palettenet: Image recolorization with given color palette. In Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR’17) Workshops. 62–70.

[29]

Mahmoud Afifi, Brian L. Price, Scott Cohen, and Michael S. Brown. 2019. Image recoloring based on object color distributions. In Eurographics (Short Papers). 33–36.

[30]

Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala. 2017. Deep photo style transfer. In Proceedings of the IEEE Conference on Computer Vision Pattern Recognition (CVPR’17). 4990–4998.

[31]

Yijun Li, Ming-Yu Liu, Xueting Li, Ming-Hsuan Yang, and Jan Kautz. 2018. A closed-form solution to photorealistic image stylization. In Proceedings of the European Conference on Computer Vision (ECCV’18). 453–468.

Digital Library

[32]

Jaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang, and Jung-Woo Ha. 2019. Photorealistic style transfer via wavelet transforms. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV’19). 9036–9045.

[33]

Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16).

[34]

Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, and Ming-Hsuan Yang. 2017. Universal style transfer via feature transforms. arXiv:1705.08086. https://arxiv.org/abs/1705.08086.

[35]

Joost Van De Weijer, Theo Gevers, and Arjan Gijsenij. 2007. Edge-based color constancy. IEEE Trans. Image Process. 16, 9 (2007), 2207–2214.

Digital Library

[36]

Tiago Carvalho, Fabio A. Faria, Helio Pedrini, Ricardo da S. Torres, and Anderson Rocha. 2015. Illuminant-based transformed spaces for image forensics. IEEE Trans. Inf. Forensics Secur. 11, 4 (2015), 720–733.

Digital Library

[37]

John S. Ho, Oscar C. Au, Jiantao Zhou, and Yuanfang Guo. 2010. Inter-channel demosaicking traces for digital image forensics. In Proceedings of the IEEE International Conference on Multimedia and Expo. 1475–1480.

[38]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. https://arxiv.org/abs/1409.1556.

[39]

Tomihisa Welsh, Michael Ashikhmin, and Klaus Mueller. 2002. Transferring color to greyscale images. In Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques. 277–280.

Digital Library

[40]

Revital Ironi, Daniel Cohen-Or, and Dani Lischinski. 2005. Colorization by example. InProceedings of the 16th Eurographics Conference on Rendering Techniques (EGSR’05). 201–210.

[41]

Raj Kumar Gupta, Alex Yong-Sang Chia, Deepu Rajan, Ee Sin Ng, and Huang Zhiyong. 2012. Image colorization using similar images. In Proceedings of the 20th ACM International Conference on Multimedia. 369–378.

Digital Library

[42]

Zezhou Cheng, Qingxiong Yang, and Bin Sheng. 2015. Deep colorization. In Proceedings of the IEEE International Conference on Computer Vision. 415–423.

Digital Library

[43]

Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2016. Learning representations for automatic colorization. In European Conference on Computer Vision. Springer, 577–593.

[44]

Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2016. Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graphics (ToG) 35, 4 (2016), 1–11.

Digital Library

[45]

Yuanfang Guo, Xiaochun Cao, Wei Zhang, and Rui Wang. 2018. Fake colorized image detection. IEEE Trans. Inf. Forensics Secur. 13, 8 (2018), 1932–1944.

[46]

Long Zhuo, Shunquan Tan, Jishen Zeng, and Bin Lit. 2018. Fake colorized image detection with channel-wise convolution based deep-learning framework. In Proceedings of the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC’18). IEEE, 733–736.

[47]

Weize Quan, Dong-Ming Yan, Kai Wang, Xiaopeng Zhang, and Denis Pellerin. 2019. Detecting colorized images via convolutional neural networks: Toward high accuracy and good generalization. arXiv:1902.06222. https://arxiv.org/abs/1902.06222.

[48]

Yangxin Yu, Ning Zheng, Tong Qiao, Ming Xu, and Jiasheng Wu. 2021. Distinguishing between natural and recolored images via lateral chromatic aberration. J. Vis. Commun. Image Rep. 80 (2021), 103295.

Digital Library

[49]

Timo Ojala, Matti Pietikainen, and Topi Maenpaa. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 7 (2002), 971–987.

Digital Library

[50]

Jianguo Zhang, Marcin Marszałek, Svetlana Lazebnik, and Cordelia Schmid. 2007. Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vis. 73, 2 (2007), 213–238.

[51]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 1–27.

Digital Library

[52]

François Pitié, Anil C. Kokaram, and Rozenn Dahyot. 2007. Automated colour grading using colour distribution transfer. Comput. Vis. Image Understand. 107, 1–2 (2007), 123–137.

Digital Library

[53]

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 3 (2015), 211–252.

Digital Library

[54]

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proceedings of the Eurpean Conference on Computer Vision (ECCV’14). 740–755.

[55]

Haodong Li, Bin Li, Shunquan Tan, and Jiwu Huang. 2020. Identification of deep network generated images using disparities in color components. Signal Process. 174 (2020), 107616.

[56]

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

[57]

Miroslav Goljan, Jessica Fridrich, and Rémi Cogranne. 2014. Rich model for steganalysis of color images. In IEEE International Workshop on Information Forensics and Security (WIFS’14). 185–190.

[58]

Shida Beigpour and Joost Van De Weijer. 2012. Object recoloring based on intrinsic image estimation. In International Conference on Computer Vision (ICCV’12). 327–334.

[59]

François Pitié and Anil Kokaram. 2007. The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer. In Proceedings of IET CVMP. 1–9.

[60]

Mairéad Grogan, Mukta Prasad, and Rozenn Dahyot. 2015. L2 registration for colour transfer. In Proceedings of the European Signal Processing Conference. 1–5.

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Zhang XXu CHan YBaciu G(2025)Fabric image recolorization by fuzzy pretrained neural networkThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03510-341:3(1907-1920)Online publication date: 1-Feb-2025
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Index Terms

Detection of Recolored Image by Texture Features in Chrominance Components
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 3

May 2023

514 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3582886

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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Publication History

Published: 25 February 2023

Online AM: 09 November 2022

Accepted: 06 November 2022

Revised: 21 October 2022

Received: 15 July 2022

Published in TOMM Volume 19, Issue 3

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Cited By

Zhang XXu CHan YBaciu G(2025)Fabric image recolorization by fuzzy pretrained neural networkThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03510-341:3(1907-1920)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s00371-024-03510-3
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
Ma JZhang FJin BSu CLi SWang ZNi J(2024)Push the Limit of Highly Accurate Ranging on Commercial UWB DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596028:2(1-27)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659602
Chen YKe QLi HWu YZhang Y(2024)xMeta: SSD-HDD-hybrid Optimization for Metadata Maintenance of Cloud-scale Object StorageACM Transactions on Architecture and Code Optimization10.1145/365260621:2(1-20)Online publication date: 21-May-2024
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Wu HWang ZLi YLiu XLee T(2024)Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon IllustrationsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365251820:7(1-26)Online publication date: 16-May-2024
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Ma YZhao CHuang BLi XBasu A(2024)RAST: Restorable Arbitrary Style TransferACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363877020:5(1-21)Online publication date: 22-Jan-2024
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