低复杂度的增强图像来源检测算法

doi:10.19665/j.issn1001-2400.2021.01.011

Abstract

Abstract:

With the rapid development of multimedia techniques,enhanced images,such as mobile phone pictures,are widely used due to its good visual quality,In general,conventional image enhancement algorithms include histogram equalization,gamma correction,and so on.Recently,a new reversible data hiding algorithm with the content enhancement function (denoted as RDH_CE) is proposed,which could achieve identical visual enhancement quality as conventional enhancement algorithms do when a certain amount of secret data is embedded.It is easy to have some security risk when one enhanced image with some suspicious code embedded in it is utilized.Therefore,an effective algorithm for identifying some suspicious RDH_CE and other regular ones (i.e.,histogram equalization and gamma correction) is proposed in this paper.By analyzing their implementation process,four features are designed and then SVM is employed to identify their source.Experimental results indicate that the proposed scheme can achieve a better performance compared with other state-of-art algorithms in terms of the accuracy and stability.

Key words: image analysis, image recognition, machine learning, image enhancement, reversible data hiding, support vector machine (SVM), image analysis, image recognition, machine learning, image enhancement, reversible data hiding, support vector machine (SVM)

CLC Number:

TP309

WANG Junxiang,HUANG Lin,ZHANG Ying,NI Jiangqun,LIN Lang. Algorithm for the detection of a low complexity contrast enhanced image source[J].Journal of Xidian University, 2021, 48(1): 96-106.

Figures/Tables 16

References 22

[1]	冯玉颖, 王俊平, 马塾亮, 等. COPLIP新模型及其图像增强新算法[J]. 西安电子科技大学学报, 2018,45(1):66-71.
	FENG Yuying, WANG Junping, MA Shuliang, et al. New COPLIP Model and Its Image Enhancement Algorithm[J]. Journal of Xidian University, 2018,45(1):66-71.
[2]	张明, 白松浩. 利用上下边界估计的图像增强方法[J]. 西安电子科技大学学报, 2015,42(1):149-154.
	ZHANG Ming, BAI Songhao. Image Enhancement Based on Estimating the Upper and Lower Boundary[J]. Journal of Xidian University, 2015,42(1):149-154.
[3]	KAMEL M S, GUAN L. Histogram Equalization Utilizing Spatial Correlation for Image Enhancement [C]//Proceedings of SPIE:1199.Bellingham:SPIE, 1989: 712-723.
[4]	WU H T, DUGELAY J L, SHI Y Q. ReversibleImage Data Hiding with Contrast Enhancement[J]. IEEE Signal Processing Letters, 2015,22(1):81-85.
[5]	WU H T, TANG S, HUANG J, et al. A Novel Reversible Data Hiding Method with Image Contrast Enhancement[J]. Signal Processing:Image Communication, 2018,62:64-73.
[6]	QIAN R, LI W, YU N, et al. Image Forensics with Rotation-tolerant Resampling Detection [C]//Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops.Washington:IEEE Computer Society, 2012: 61-66.
[7]	KUMAWAT C, PANKAJAKSHAN V. A Robust JPEG Compression Detector for Image Forensics[J]. Signal Processing:Image Communication, 2020,89:116008.
[8]	BHARDWAJ D, PANKAJAKSHAN V. A JPEG Blocking Artifact Detector for Image Forensics[J]. Signal Processing:Image Communication, 2018,68:155-161.
[9]	SHAN W, YI Y, QIU J, et al. Robust Median Filtering Forensics Using Image Deblocking and Filtered Residual Fusion[J]. IEEE Access, 2019,7:17174-17183.
[10]	YUANH D. Blind Forensics of Median Filtering in Digital Images[J]. IEEE Transactions on Information Forensics and Security, 2011,6(4):1335-1345.
[11]	HUANG H Y, CIOU A J. Copy-move Forgery Detection for Image Forensics Using the Superpixel Segmentation and the Helmert Transformation[J]. EURASIP Journal on Image and Video Processing, 2019,2019(1):68.
[12]	LI Y M, ZHOU J. Fast and Effective Image Copy-move Forgery Detection via Hierarchical Feature Point Matching[J]. IEEE Transactions on Information Forensics and Security, 2019,14(5):1307-1322.
[13]	WU Y, ABD-ALMAGEED W, NATARAJAN P. Image Copy-move Forgery Detection via an End-to-end Deep Neural Network [C] //Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision.Piscataway:IEEE, 2018: 1907-1915.
[14]	STAMM MC, LIU K J R.Forensic Detection of Image Manipulation Using Statistical Intrinsic Fingerprints[J]. IEEE Transactions on Information Forensics and Security, 2010,5(3):492-506.
[15]	CAO G, ZHAO Y, NI R, et al. Contrast Enhancement-based Forensics in Digital Images[J]. IEEE Transactions on Information Forensics and Security, 2014,9(3):515-525.
[16]	DE ROSA A, FONTANI M, MASSAI M, et al. Second-order Statistics Analysis to Cope with Contrast Enhancement Counter-forensics[J]. IEEE Signal Processing Letters, 2015,22(8):1132-1136.
[17]	SUN J Y, KIM S W, LEE S W, et al. A Novel Contrast Enhancement Forensics Based on Convolutional Neural Networks[J]. Signal Processing:Image Communication, 2018,63:149-160.
[18]	SHAN W, YI Y, HUANG R, et al. Robust Contrast Enhancement Forensics Based on Convolutional Neural Networks[J]. Signal Processing:Image Communication, 2019,71:138-146.
[19]	CHEN Y, LYU Z X, KANG X, et al. A Rotation-invariant Convolutional Neural Network for Image Enhancement Forensics [C]// Proceedings of the 2018 IEEE International Conference on Acoustics,Speech and Signal Processing.Piscataway:IEEE, 2018: 2111-2115.
[20]	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.
[21]	BAS P, FILLER T, PEVNY T. Break Our Steganographic System:the Ins and Outs of Organizing BOSS [C]//Lecture Notes in Computer Science:6958.Heidelberg:Springer Verlag, 2011: 59-70.
[22]	GONEN, M. Receiver Operating Characteristic (ROC) Curves[J]. Journal of Clinical Nursing (Wiley-Blackwell), 2002,11(1):136.

特征	直方图均匀化	伽马矫正	RDH_CE
零点个数	增加	增加	减少
零点分布	分散于直方图中	集中在单侧	分散在直方图两侧
包络线	整体平缓(中间散落零点)	存在单侧峰(起伏大)	整体平缓(中间无零点)

特征组合	HE、G、RDH_0.03	HE、G、RDH_0.10	HE、G、RDH_0.70
f₁	67.70	72.56	81.44
f₁+f₂	92.84	93.05	96.32
f₁+f₂+f₃	93.50	93.95	97.18
f₁+f₂+f₃+f₄	95.75	96.15	97.39

检测对象	文献[14]	文献[15]	文献[16]	文中算法
HE、G、RDH_0.005	69.23	68.79	81.52	95.41
HE、G、RDH_0.030	56.67	68.78	81.57	95.75
HE、G、RDH_0.050	63.22	68.76	81.85	95.95
HE、G、RDH_0.100	64.90	68.75	80.33	96.15
HE、G、RDH_0.500	67.08	67.49	83.10	97.43
HE、G、RDH_0.700	65.90	68.53	85.40	97.39

检测对象	文献[14]	文献[15]	文献[16]	文中算法
O与RDH_0.005	91.06	64.00	76.99	95.26
O与RDH_0.030	93.18	65.81	78.48	95.05
O与RDH_0.050	93.81	66.65	77.96	95.09
O与RDH_0.100	95.31	67.98	77.84	95.18
O与RDH_0.500	98.65	76.14	84.44	95.78
O与RDH_0.700	98.93	79.02	87.60	96.39

检测对象	文献[14]	文献[15]	文献[16]	文中算法
O与HE	99.15	98.40	99.27	99.92
O与G_0.5	99.00	99.20	92.87	99.46
O与G_0.7	99.40	99.46	94.60	99.20
O与G_0.9	97.26	99.06	84.00	93.70
O与G_1.1	94.86	88.67	79.27	90.67
O与G_1.3	93.20	91.60	81.30	89.80
O与G_1.5	98.13	92.80	90.86	98.47
O与G_2.0	92.70	88.40	81.00	97.30

Algorithm for the detection of a low complexity contrast enhanced image source

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 22

Related Articles 15

Metrics

Comments

Recommended 10

[1]	QIAO Wenxin,LU Yu,LIU Yicen,LI Zhiwei,LI Xi. Dynamic scheduling method for service function chains in space air terrestrial aided edge cloud networks [J]. Journal of Xidian University, 2022, 49(2): 79-88.
[2]	SUN Yanjing,WEI Li,ZHANG Nianlong,YUN Xiao,DONG Kaiwen,GE Min,CHENG Xiaozhou,HOU Xiaofeng. Person re-identification method combining the DD-GAN and Global feature in a coal mine [J]. Journal of Xidian University, 2021, 48(5): 201-211.
[3]	ZENG Yong,WU Zhengyuan,DONG Lihua,LIU Zhihong,MA Jianfeng,LI Zan. Research on malicious traffic identification technology in encrypted traffic [J]. Journal of Xidian University, 2021, 48(3): 170-187.
[4]	QIU Yingqiang,CAI Canhui,ZENG Huanqiang,FENG Gui,LIN Xiaodan,QIAN Zhenxing. Joint reversible data hiding in encrypted images with the self-correcting ability [J]. Journal of Xidian University, 2021, 48(1): 107-116.
[5]	ZHANG Shudong,GAO Haichang,CAO Xiwen,KANG Shuai. Adaptive fast and targeted adversarial attack for speech recognition [J]. Journal of Xidian University, 2021, 48(1): 168-175.
[6]	WANG Xiaoming,ZHANG Shuyan,ZHANG Jie,YUAN Sicong. Contour reconstruction method for noisy image based on depth residual learning [J]. Journal of Xidian University, 2020, 47(3): 66-71.
[7]	YAN Lin,LIU Kai,DUAN Meiyu. Lightweight deep neural network for point cloud classification [J]. Journal of Xidian University, 2020, 47(2): 46-53.
[8]	LIU Zhiqiang,ZHANG Mingjin,CHI Yuan,LI Yunsong. Hardware Trojan detection algorithm based on deep learning [J]. Journal of Xidian University, 2019, 46(6): 37-45.
[9]	MA Miao;XU Xidan;WU Jie. Coupled-deep belief network based method for image recognition [J]. Journal of Xidian University, 2018, 45(5): 102-107.
[10]	FENG Yuying;WANG Junping;MA Shuliang;LI Chao;WU Yao. New COPLIP model and its image enhancement algorithm [J]. Journal of Xidian University, 2018, 45(1): 66-71.
[11]	ZHANG Ming;BAI Songhao. Image enhancement based on estimating the upper and lower boundary [J]. J4, 2015, 42(1): 149-154.
[12]	CUI Hua;ZHOU Tingjie;GUO Lu;YUAN Chao;SONG Huansheng. Image enhancement using the improved partial differential equation [J]. J4, 2014, 41(6): 195-198.
[13]	LI Juan;WANG Yuping. New nearest neighbor affinity similarity function based on separation and compactness between samples [J]. J4, 2014, 41(3): 123-130.
[14]	LI Jin;WANG Junping;WAN Guoting;LI Ziyang;XU Dan;CAO Honghua;ZHANG Guangyan. Novel algorithm for image enhancement with histogram equalization and MSRCR [J]. J4, 2014, 41(3): 103-109.
[15]	ZHANG Guangyan;WANG Junping;WANG Song;CAO Honghua;XU Dan;LI Jin;LI Changjiang. New image enhancement algorithm based on the SLIP model [J]. J4, 2014, 41(1): 105-109+169.