在图像篡改中常使用几何变换、JPEG(Joint Photographic Experts Group)压缩以及模糊操作,其特性是图像伪作检测的依据.首先定义兼顾重采样和JPEG压缩特性的块度量因子,将待测图像重叠分块计算块度量因子,利用其值的不一致性来检测定位篡改区域.实验结果表明,与现有针对性单一的检测方法相比,该方法可以检测更多篡改组合模式下的篡改操作并能有效定位出篡改区域,且对于有损JPEG压缩具有较好的鲁棒性.其次,提出一种检测模糊痕迹的方法.利用一定的模糊核对待测图像进行再次模糊,计算模糊前后两图像的像素差值,根据差值图像值的不同分类完成模糊篡改区域的定位.实验结果表明,该方法能实现对不同模糊方式的盲检测,且对JPEG压缩的抵抗能力较好,同时与现有基于分块检测的方法相比,大大降低了计算复杂度且能检测出较细小的模糊痕迹.%In image forging, geometric transformations, JPEG compression, and blurring are typical operations. In this manuscript, algorithms for detection of the typical operations in a forged image are proposed based on operational characteristics. First, a possibly composite image is divided into overlapping blocks, and a block measure factor is defined and adopted to describe both re-sampling and JPEG compression characteristics for each block, followed by detection of tampered regions. Experimental results show that compared with the existing single targeted detection methods, the proposed algorithm can recognize forged images under more combinations of tampering modes and the tampered regions are located more effectively. Furthermore, the proposed method performs well even when the JPEG quality factor is small. Second, an approach is proposed to detect blurring traces. The image is blurred again with an appropriate blurring kernel and the difference of image pixels are estimated before and after double blurring. The tampered regions may be detected through the classification of the values in the difference image. Simulation results show that the algorithm is effective for blurring detection with various blurring operations and it also robust against lossy JPEG compression. Comparing with the existing block-based methods, the proposed method can reduce the computational complexity greatly by avoiding the point-by-point block calculation, and can detect small fuzzy traces.
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