基于灰度差分统计法的图像复制与移动伪造检测方法
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摘要
针对数字图像中复制-移动伪造的检测,提出一种结合灰度差分统计法(GDS)特征提取和多层感知机(MLP)神经网络分类器的检测方法。首先,将图像转换为灰度图像,并获得图像的灰度差分矩阵。然后,根据灰度分布、距离分布等信息,利用灰度差分统计法构建5个特征构成特征向量。最后,基于提取的特征,通过MLP神经网络进行分类,以此来检测该图像块是否为伪造区域。在实验中,将提出的GDS特征与传统SIFT特征进行了比较,结果表明,该方法能够有效检测出伪造区域,具有较高的准确性。
For the detection of copy-move forgery in digital images,a detection method is proposed based on gray-level difference statistics(GDS)feature extraction and multi-layer perceptron(MLP)neural network classifier.Firstly,the image is converted to a gray image and the gray difference matrix of the image is obtained.Then,based on the gray-level difference distribution and distance distribution,five features are extracted by using the gray-level difference statistics to form an eigenvector.Finally,based on the extracted features,classification is performed by the MLP neural network to detect whether the image block is a forged region.In the experiment,the proposed GDS features are compared with the traditional SIFT features.The results show that this method can effectively detect fake regions with high accuracy.
For the detection of copy-move forgery in digital images,a detection method is proposed based on gray-level difference statistics(GDS)feature extraction and multi-layer perceptron(MLP)neural network classifier.Firstly,the image is converted to a gray image and the gray difference matrix of the image is obtained.Then,based on the gray-level difference distribution and distance distribution,five features are extracted by using the gray-level difference statistics to form an eigenvector.Finally,based on the extracted features,classification is performed by the MLP neural network to detect whether the image block is a forged region.In the experiment,the proposed GDS features are compared with the traditional SIFT features.The results show that this method can effectively detect fake regions with high accuracy.
引文
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[2]邵虹,朱虹,崔文成.抗翻转、旋转和缩放攻击的图像区域复制篡改检测[J].计算机辅助设计与图形学学报,2015,27(1):157-165.
[3]李晓飞,李鹏飞.基于SIFT的伪造图像盲检测算法[J].长春大学学报:自然科学版,2014,24(10):1354-1357.
[4]Tu K H,Huynh K V,Le-Tien T,et al.A survey on Image Forgery Detection techniques[C]//IEEE Rivf International Conference on Computing&Communication Technologies-Research,Innovation,and Vision for the Future.IEEE,2015:71-76.
[5]Li L,Li S,Zhu H,et al.An efficient scheme for detecting copy-move forged images by local binary patterns[J].Journal of Information Hiding&Multimedia Signal Processing,2013,4(1):46-56.
[6]Yang J,Ran P,Xiao D,et al.Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments[J].Journal of Computational Information Systems,2013,9(16):6399-6408.
[7]Kashyap A,Joshi S D.Detection of copy-move forgery using wavelet decomposition[C]//International Conference on Signal Processing and Communication.IEEE,2014:396-400.
[8]刘辉,张云生,张印辉,等.基于灰度差分统计的火焰图像纹理特征提取[J].控制工程,2013,20(2):213-218.
[9]万雪,张祖勋,柯涛.一种利用零交叉点理论的改进SIFT特征提取算法[J].武汉大学学报(信息科学版),2013,38(3):270-273.
[10]Rajkumar R,Singh K M.Digital image forgery detection using SIFT feature[C]//International Symposium on Advanced Computing and Communication.IEEE,2016:186-191.
[11]Hsu C Y,Lu C S,Pei S C.Image feature extraction in encrypted domain with privacy-preserving SIFT[J].IEEE Trans Image Process,2012,21(11):4593-4607.
[12]李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260.
[13]Sambhaji E S.Leaf Recognition Algorithm Using MLP Neural Network Based Image Processing[J].International Journal of Engineering Sciences&Research Technology,2014,3(5):132-137.