CAJ | 학술논문

随着图形编辑软件的普及，数字图像篡改越来越容易，数字图像篡改检测已成为一个亟需解决的问题。为此，提出基于图片信息熵和多步马尔可夫特征的图像拼接检测方法。该方法将图像拼接检测问题转换为两分类模式识别问题，先从原图、3阶Haar离散小波变换(DWT)和多尺度分块离散余弦变换(DCT)中提取图片的信息熵，再从图像的分块DCT系数中提取多步马尔可夫转移概率矩阵，由信息熵和多步马尔可夫转移概率矩阵组成统计特征，利用支持向量机分类器进行分类得到判决结果。实验结果表明，该方法在哥伦比亚图片库上具有较高的拼接检测精度，达到89.91%。
수착도형편집연건적보급，수자도상찬개월래월용역，수자도상찬개검측이성위일개극수해결적문제。위차，제출기우도편신식적화다보마이가부특정적도상병접검측방법。해방법장도상병접검측문제전환위량분류모식식별문제，선종원도、3계Haar리산소파변환(DWT)화다척도분괴리산여현변환(DCT)중제취도편적신식적，재종도상적분괴DCT계수중제취다보마이가부전이개솔구진，유신식적화다보마이가부전이개솔구진조성통계특정，이용지지향량궤분류기진행분류득도판결결과。실험결과표명，해방법재가륜비아도편고상구유교고적병접검측정도，체도89.91%。
With the popularity of graphic editing software, tampering a digital image becomes more and more easier, so it is urgent to solve digital image forensics problem. Aiming at the problem, this paper proposes an image splicing detection method based on image information entropy feature and multi-step Markov feature. Image splicing detection can be treated as a two-class pattern recognition problem. This method consists of entropy feature extracted from the original image, three-level Haar Discrete Wavelet Transform(DWT) and multiple-size block Discrete Cosine Transform(DCT), and multi-step Markov feature transition probability matrix is extracted from block DCT. The statistical characteristics consist of information entropy and multi-step Markov feature. Support Vector Machine(SVM) is used to judge the image category and get judgment result. Experimental results show that the proposed method applied to the Columbia image dataset possesses promising capability in splicing detection, and it can achieve a detection accuracy of 89.91%.