噪声水平不一致性的图像拼接区域检测方法
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摘要
针对图像拼接篡改检测与篡改定位技术进行研究,提出了一种噪声水平不一致性的图像拼接篡改定位方法.该方法利用改进的拉普拉斯算子对噪声具有双倍加强作用的特点,结合奇异值分解,提取非重叠图像块的局部图像梯度矩阵和噪声特征,然后利用基于聚类的阈值算法,对得到的特征进行分类并定位出篡改区域.与现有的基于噪声的图像拼接区域检测方法相比,所提出的方法不仅能够检测拼篡改区域,而且当拼接区域与原始区域之间的噪声差异较小时依然有效,并且对于内容保持的图像处理操作如JPEG压缩、高斯模糊、伽玛校正、下采样等是鲁棒的.
Focusing on image splicing detection and splicing localization, we proposed an image splicing region detection method of noise level inconsistency. In the proposed method, we utilize the double enhancement effect of the improved Laplace operator on noise, and combine the singular value decomposition to extract the local image gradient matrix and noise features from non-overlapping image blocks. Then, we use a clustering-based threshold algorithm to classify the noise features and locate the tampered regions. Compared with the existing noise-based image splicing region detection method, the proposed method has superior performance, especially when the noise difference between the splicing region and the original region is less. In addition, the proposed method is robust to content maintenance operation such as JPEG compression, Gaussian blur, Gamma correction, down sampling, and so on.
Focusing on image splicing detection and splicing localization, we proposed an image splicing region detection method of noise level inconsistency. In the proposed method, we utilize the double enhancement effect of the improved Laplace operator on noise, and combine the singular value decomposition to extract the local image gradient matrix and noise features from non-overlapping image blocks. Then, we use a clustering-based threshold algorithm to classify the noise features and locate the tampered regions. Compared with the existing noise-based image splicing region detection method, the proposed method has superior performance, especially when the noise difference between the splicing region and the original region is less. In addition, the proposed method is robust to content maintenance operation such as JPEG compression, Gaussian blur, Gamma correction, down sampling, and so on.
引文
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6 Pun CM,Liu B,Yuan XC.Multi-scale noise estimation for image splicing forgery detection.Journal of Visual Communication and Image Representation,2016,38:195-206.[doi:10.1016/j.jvcir.2016.03.005]
7 Zeng H,Zhan YF,Kang XG,et al.Image splicing localization using PCA-based noise level estimation Multimedia Tools and Applications,2016,76(4):4783-4799
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11 Russo F.A method for estimation and filtering of Gaussian noise in images.IEEE Transactions on Instrumentation and Measurement,2003,52(4):1148-1154.[doi:10.1109/TIM2003.815989]
12 Immerk?r J.Fast noise variance estimation.Compute Vision and Image Understanding,1996,64(2):300-302[doi:10.1006/cviu.1996.0060]
13 Liu W,Lin WS.Additive white Gaussian noise leve estimation in SVD domain for images.IEEE Transactions on Image Processing,2013,22(3):872-883.[doi:10.1109TIP.2012.2219544]
14宫改云,高新波,伍忠东.FCM聚类算法中模糊加权指数m的优选方法.模糊系统与数学,2005,19(1):143-148[doi:10.3969/j.issn.1001-7402.2005.01.025]
15汤效琴,戴汝源.数据挖掘中聚类分析的技术方法.微计算机信息,2003,(1):3-4.[doi:10.3969/j.issn.1008-0570.200301.002]
2 Liu C,Freeman WT,Szeliski R,et al.Noise estimation from a single image.2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York,NYUSA.2006.901-908.
3 Liu C,Szeliski R,Kang SB,et al.Automatic estimation and removal of noise from a single image.IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(20):299-314.
4 Yang JY,Wu ZY,Hou CP.Estimation of signal-dependent sensor noise via sparse representation of noise level functions.2012 19th IEEE International Conference on Image Processing.Orlando,FL,USA.2013.673-676.
5 Lyu S,Pan XY,Zhang X.Exposing region splicing forgeries with blind local noise estimation.International Journal of Computer Vision,2014,110(2):202-221.[doi:10.1007/s11263-013-0688-y]
6 Pun CM,Liu B,Yuan XC.Multi-scale noise estimation for image splicing forgery detection.Journal of Visual Communication and Image Representation,2016,38:195-206.[doi:10.1016/j.jvcir.2016.03.005]
7 Zeng H,Zhan YF,Kang XG,et al.Image splicing localization using PCA-based noise level estimation Multimedia Tools and Applications,2016,76(4):4783-4799
8 Chierchia G,Poggi G,Sansone C,et al.A Bayesian-MRFapproach for PRNU-based image forgery detection.IEEETransactions on Information Forensics and Security,20149(4):554-567.[doi:10.1109/TIFS.2014.2302078]
9 Yao H,Wang SZ,Zhang XP,et al.Detecting image splicing based on noise level inconsistency.Multimedia Tools and Applications,2017,76(10):12457-12479.[doi:10.1007/11042-016-3660-3]
10 Cozzolino D,Verdoliva L.Single-image splicing localization through autoencoder-based anomaly detection.IEEEInternational Workshop on Information Forensics and Security.Abu Dhabi,United Arab Emirates.2016:1-6.
11 Russo F.A method for estimation and filtering of Gaussian noise in images.IEEE Transactions on Instrumentation and Measurement,2003,52(4):1148-1154.[doi:10.1109/TIM2003.815989]
12 Immerk?r J.Fast noise variance estimation.Compute Vision and Image Understanding,1996,64(2):300-302[doi:10.1006/cviu.1996.0060]
13 Liu W,Lin WS.Additive white Gaussian noise leve estimation in SVD domain for images.IEEE Transactions on Image Processing,2013,22(3):872-883.[doi:10.1109TIP.2012.2219544]
14宫改云,高新波,伍忠东.FCM聚类算法中模糊加权指数m的优选方法.模糊系统与数学,2005,19(1):143-148[doi:10.3969/j.issn.1001-7402.2005.01.025]
15汤效琴,戴汝源.数据挖掘中聚类分析的技术方法.微计算机信息,2003,(1):3-4.[doi:10.3969/j.issn.1008-0570.200301.002]