目的 为了解决现有图像区域复制篡改检测算法只能识别图像中成对的相似区域而不能准确定位篡改区域的问题,提出一种基于JPEG(joint photographic experts group)图像双重压缩偏移量估计的篡改区域自动检测定位方法。方法 首先利用尺度不变特征变换(SIFT)算法提取图像的特征点和相应的特征向量,并采用最近邻算法对特征向量进行初步匹配,接下来结合特征点的色调饱和度(HSI)彩色特征进行优化匹配,消除彩色信息不一致引发的误匹配;然后利用随机样本一致性(RANSAC)算法对匹配对之间的仿射变换参数进行估计并消除错配,通过构建区域相关图确定完整的复制粘贴区域;最后根据对复制粘贴区域分别估计的JPEG双重压缩偏移量区分复制区域和篡改区域。结果 与经典SIFT和SURF(speeded up robust features)的检测方法相比,本文方法在实现较高检测率的同时,有效降低了检测虚警率。当第2次JPEG压缩的质量因子大于第1次时,篡改区域的检出率可以达到96%以上。 结论 本文方法可以有效定位JPEG图像的区域复制篡改区域,并且对复制区域的几何变换以及常见的后处理操作具有较强的鲁棒性。

Objective Existing algorithms for detecting image copy-paste forgery can identify pairwise-similar regions in suspicious images but cannot accurately locate tampered regions. To address such drawback, we proposed a novel method for the automatic detection and localization of tampered regions on the basis of the offset estimation of double JPEG(Joint Photographic Experts Group)image compression. Method First, key points and their corresponding feature vectors were extracted using the SIFT(scale invariant features tromsform) algorithm, and a preliminary match of the feature vectors was implemented with the nearest neighbor algorithm. Second, the mismatch caused by inconsistent color information was eliminated by combining the preliminary matching points with the HSI(hue saturation intensity) color features to optimize the initial matching key points. Third, the matched key points were used to estimate the affine transform parameters and to eliminate the mismatching points with the RANSAC(random sample consensus) algorithm. Fourth, all the pixels within the copy-move regions were obtained by building a region correlation map. Finally, the duplicated region and the forgery region were distinguished from the copy-move regions according to the estimated offset of the double JPEG image compression. Result Compared with the classical SIFT and SURF(speeded up robust features) detection methods, our method can achieve higher true positive rates and effectively reduce false positive rates. When the second JPEG compression quality factor is greater than the first, the rate of forgery region detection can reach more than 96%. Conclusion The proposed approach can effectively locate the forgery regions of JPEG images counterfeited via copy-paste tampering. It also demonstrates excellent robustness for copied regions distorted by geometric transformations and common post-processing operations.