基于通道间相关性的图像重着色检测

doi:10.11959/j.issn.2096-109x.2022057

Abstract

Abstract:

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values.With the rapid proliferation of social networks and image editing techniques, recolored images have seriously hampered the authenticity of the communicated information.However, there are few works specifically designed for image recoloring.Existing recoloring detection methods still have much improvement space in conventional recoloring scenarios and are ineffective in dealing with hand-crafted recolored images.For this purpose, a recolored image detection method based on inter-channel correlation was proposed for conventional recoloring and hand-crafted recoloring scenarios.Based on the phenomenon that there were significant disparities between camera imaging and recolored image generation methods, the hypothesis that recoloring operations might destroy the inter-channel correlation of natural images was proposed.The numerical analysis demonstrated that the inter-channel correlation disparities can be used as an important discriminative metric to distinguish between recolored images and natural images.Based on such new prior knowledge, the proposed method obtained the inter-channel correlation feature set of the image.The feature set was extracted from the channel co-occurrence matrix of the first-order differential residuals of the differential image.In addition, three detection scenarios were assumed based on practical situations, including scenarios with matching and mismatching between training-testing data, and scenario with hand-crafted recoloring.Experimental results show that the proposed method can accurately identify recolored images and outperforms existing methods in all three hypothetical scenarios, achieving state-of-the-art detection accuracy.In addition, the proposed method is less dependent on the amount of training data and can achieve fairly accurate prediction results with limited training data.

Key words: image recoloring, forgery detection, inter-channel correlation, image forensics

CLC Number:

TP393

Nuo CHEN, Shuren QI, Yushu ZHANG, Mingfu XUE, Zhongyun HUA. Image recoloring detection based on inter-channel correlation[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 167-178.

Figures/Tables 15

方法	准确率	AUC
RecDeNet	83.50%	90.75%
SC-Method	77.00%	84.35%
RF-Method	69.00%	—
DCC-Method	51.50%	50.18%
ICC-Method	$90.50 %$	$94.68 %$

方法	准确率
RecDeNet	65.00%
SC-Method	70.00%
RF-Method	37.50%
DCC-Method	82.50%
ICC-Method	$85.00 %$

References 46

[1]	NIGHTINGALE S J , WADE K A , WATSON D G . Can people identify original and manipulated photos of real-world scenes[J]. Cognitive Research:Principles and Implications, 2017,2(1): 30.
[2]	VERDOLIVA L . Media forensics and DeepFakes:an overview[J]. IEEE Journal of Selected Topics in Signal Processing, 2020,14(5): 910-932.
[3]	REINHARD E , ADHIKHMIN M , GOOCH B ,et al. Color transfer between images[J]. IEEE Computer Graphics and Applications, 2001,21(5): 34-41.
[4]	PITIE F , KOKARAM A C , DAHYOT R . N-dimensional probability density function transfer and its application to color transfer[C]// Proceedings of Tenth IEEE International Conference on Computer Vision (ICCV'05). 2005: 1434-1439.
[5]	TAI Y W , JIA J , TANG C K . Local color transfer via probabilistic segmentation by expectation-maximization[C]// Proceedings of 2005 IEEE Conference on Computer Vision and Pattern Recognition. 2005: 747-754.
[6]	BEIGPOUR S , VAN DE WEIJER J . Object recoloring based on intrinsic image estimation[C]// Proceedings of 2011 International Conference on Computer Vision. 2011: 327-334.
[7]	PITIé F , KOKARAM A C , DAHYOT R . Automated colour grading using colour distribution transfer[J]. Computer Vision and Image Understanding, 2007,107(1/2): 123-137.
[8]	PITIé F , KOKARAM A , . The linear monge-kantorovitch linear colour mapping for example-based colour transfer[C]// European Conference on Visual Media Production. 2007: 1-9.
[9]	GROGAN M , PRASAD M , DAHYOT R . L2 registration for colour transfer[C]// Proceedings of 2015 23rd European Signal Processing Conference (EUSIPCO). 2015: 1-5.
[10]	LEVIN A , LISCHINSKI D , WEISS Y . Colorization using optimization[M]// ACM Siggraph, 2004: 689-694.
[11]	QU Y , WONG T T , HENG P A . Manga colorization[J]. ACM Transactions on Graphics (TOG), 2006,25(3): 1214-1220.
[12]	AN X B , PELLACINI F . AppProp:all-pairs appearance-space edit propagation[C]// Proceedings of ACM SIGGRAPH 2008. 2008: 1-9.
[13]	XU K , LI Y , JU T ,et al. Efficient affinity-based edit propagation using KD tree[J]. ACM Transactions on Graphics (TOG), 2009,28(5): 1-6.
[14]	CHEN X W , ZOU D Q , LI J W ,et al. Sparse dictionary learning for edit propagation of high-resolution images[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014: 2854-2861.
[15]	O'DONOVAN P , AGARWALA A , HERTZMANN A . Color compatibility from large datasets[M]// ACM Siggraph, 2011: 1-12.
[16]	LIN S , RITCHIE D , FISHER M ,et al. Probabilistic color-by-numbers:Suggesting pattern colorizations using factor graphs[J]. ACM Transactions on Graphics (TOG), 2013,32(4): 1-12.
[17]	CHANG H , FRIED O , LIU Y ,et al. Palette-based photo recoloring[J]. ACM Transactions on Graphics (TOG), 2015,34(4): 1-11.
[18]	TSAI Y H , SHEN X H , LIN Z ,et al. Sky is not the limit:semantic-aware sky replacement[J]. ACM Transactions on Graphics, 2016,35(4): 1-11.
[19]	LEE J Y , SON H , LEE G ,et al. Deep color transfer using histogram analogy[J]. The Visual Computer, 2020,36(10): 2129-2143.
[20]	HE M , LIAO J , CHEN D ,et al. Progressive color transfer with dense semantic correspondences[J]. ACM Transactions on Graphics (TOG), 2019,38(2): 1-18.
[21]	ENDO Y , IIZUKA S , KANAMORI Y ,et al. DeepProp:extracting deep features from a single image for edit propagation[J]. Computer Graphics Forum, 2016,35(2): 189-201.
[22]	ZHANG R , ZHU J Y , ISOLA P ,et al. Real-time user-guided image colorization with learned deep priors[J]. arXiv preprint arXiv:1705.02999, 2017.
[23]	CHO J , YUN S , LEE K ,et al. PaletteNet:image recolorization with given color palette[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017: 1058-1066.
[24]	AFIFI M , PRICE B L , COHEN S ,et al. Image recoloring based on object color distributions[C]// Eurographics (Short Papers). 2019: 33-36.
[25]	LUAN F , PARIS S , SHECHTMAN E ,et al. Deep photo style transfer[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017: 4990-4998.
[26]	LI Y J , LIU M Y , LI X T ,et al. A closed-form solution to photorealistic image stylization[C]// Proceedings of the European Conference on Computer Vision. 2018: 453-468.
[27]	YOO J , UH Y , CHUN S ,et al. Photorealistic style transfer via wavelet transforms[C]// Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019: 9035-9044.
[28]	BIRAJDAR G K , MANKAR V H . Digital image forgery detection using passive techniques:a survey[J]. Digital Investigation, 2013,10(3): 226-245.
[29]	吴金海, 林福宗 . 基于数字水印的图像认证技术[J]. 计算机学报, 2004,27(9): 1153-1161.
	WU J H , LIN F Z . Image authentication based on digital watermarking[J]. Chinese Journal of Computers, 2004,27(9): 1153-1161.
[30]	YERUSHALMY I , HEL-OR H , . Digital image forgery detection based on lens and sensor aberration[J]. International Journal of Computer Vision, 2011,92(1): 71-91.
[31]	POPESCU A C , FARID H . Statistical tools for digital forensics[C]// International Workshop on Information Hiding. 2004: 128-147.
[32]	LYU S W , PAN X Y , ZHANG X . Exposing region splicing forgeries with blind local noise estimation[J]. International Journal of Computer Vision, 2014,110(2): 202-221.
[33]	MAHDIAN B , SAIC S . Using noise inconsistencies for blind image forensics[J]. Image and Vision Computing, 2009,27(10): 1497-1503.
[34]	FRIDRICH A J , SOUKAL B D , LUKá? A J , . Detection of copy-move forgery in digital images[C]// Digital Forensic Research Workshop. 2003.
[35]	YAN Y Y , REN W Q , CAO X C . Recolored image detection via a deep discriminative model[J]. IEEE Transactions on Information Forensics and Security, 2019,14(1): 5-17.
[36]	ZHANG Y S , CHEN N , QI S R ,et al. Detecting recolored image by spatial correlation[J]. arXiv preprint arXiv:2204.10973, 2022.
[37]	HO J S , AU O C , ZHOU J T ,et al. Inter-channel demosaicking traces for digital image forensics[C]// Proceedings of 2010 IEEE International Conference on Multimedia and Expo. 2010: 1475-1480.
[38]	VAN DE WEIJER J , GEVERS T , GIJSENIJ A . Edge-based color constancy[J]. IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society, 2007,16(9): 2207-2214.
[39]	CARVALHO T , FARIA F A , PEDRINI H ,et al. Illuminant-based transformed spaces for image forensics[J]. IEEE Transactions on Information Forensics and Security, 2016,11(4): 720-733.
[40]	SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
[41]	GUNTURK B K , ALTUNBASAK Y , MERSEREAU R M . Color plane interpolation using alternating projections[J]. IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society, 2002,11(9): 997-1013.
[42]	RUSSAKOVSKY O , DENG J , SU H ,et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015,115(3): 211-252.
[43]	CHANG C C , LIN C J . LIBSVM:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011,2(3): 27.
[44]	GOLJAN M , FRIDRICH J , COGRANNE R . Rich model for Steganalysis of color images[C]// Proceedings of 2014 IEEE International Workshop on Information Forensics and Security. 2014: 185-190.
[45]	LI H D , LI B , TAN S Q ,et al. Identification of deep network generated images using disparities in color components[J]. Signal Processing, 2020,174:107616.
[46]	LIN T Y , MAIRE M , BELONGIE S ,et al. Microsoft coco:common objects in context[C]// European Conference on Computer Vision. 2014: 740-755.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	用途	重着色方法	重着色图像数目	自然图像来源	自然图像数目
D₁	训练	文献[3]	18 000	ImageNet^[42]	18 000
		文献[7]	18 000	ImageNet^[42]	18 000
D₂	测试	文献[3]	60	ImageNet^[42]	60
		文献[7]	60	ImageNet^[42]	60
D₃	测试	文献[3,6-9,12,17]	100	下载自网络	100
D₄	测试	手工制作	80	—	—

测试集	准确率	AUC
D₂	93.75%	96.88%
D₃	90.50%	94.68%
D₄	85.00%	—

Image recoloring detection based on inter-channel correlation

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 46

Related Articles 6

Metrics

Recommended 0

[1]	Dengyong ZHANG, Huang WEN, Feng LI, Peng CAO, Lingyun XIANG, Gaobo YANG, Xiangling DING. Image inpainting forensics method based on dual branch network [J]. Chinese Journal of Network and Information Security, 2022, 8(6): 110-122.
[2]	Jiaying LIN, Wenbo ZHOU, Weiming ZHANG, Nenghai YU. Lip forgery detection via spatial-frequency domain combination [J]. Chinese Journal of Network and Information Security, 2022, 8(6): 146-155.
[3]	Tong QIAO, Hongwei YAO, Binmin PAN, Ming XU, Yanli CHEN. Research progress of digital image forensic techniques based on deep learning [J]. Chinese Journal of Network and Information Security, 2021, 7(5): 13-28.
[4]	Hui-fen HUANG,Zhi-hong WANG,Yu-hong CHANG. Blur detection of digital forgery using mathematical morphology [J]. Chinese Journal of Network and Information Security, 2017, 3(4): 20-25.
[5]	Wei HUANG,Tian-qiang HUANG,Xue-li ZHANG,Hui XIAO. JPEG recapture image tamper detection method based on block effect grid offset [J]. Chinese Journal of Network and Information Security, 2017, 3(12): 38-44.
[6]	Bing-wen FENG,Jian WENG,Wei LU. Research on outsourcing of digital image forensics based on privacy preserving [J]. Chinese Journal of Network and Information Security, 2016, 2(8): 23-31.