目的 图像区域复制粘贴篡改是目前众多图像篡改技术中一种简单而且常见的方式。针对目前大多数区域复制粘贴篡改检测算法鲁棒性不强,提出一种基于指数矩的图像篡改检测算法。方法 首先将图像分成重叠的图像子块,然后提取每一图像子块的指数矩作为特征向量进行字典排序,利用向量相似度和位移初步确定疑似图像子块,再根据疑似图像子块的相邻子块个数和角度方差去除误匹配块,得到最终篡改区域。结果 该算法具有良好的鲁棒性,与采用圆谐-傅里叶矩的算法相比,在图像受到噪声干扰时,检测率平均提高26.66%,错误率平均降低33.77%。结论 本文算法利用图像的指数矩,针对图像区域复制粘贴篡改操作,能有效检测出图像的篡改区域。检测图像在经过旋转、高斯模糊和添加噪声等后期处理时,算法依然有效。

Objective An image can be easily tampered given the development of digital imaging technologies nowadays. Copy-move forgery is one of the most commonly and easily used tampering techniques. To make the tampered image look normal, the copied region may be subjected to various post-processing operations. However, most existing methods for detecting altered regions are too sensitive to the post-processing operations. As most existing copy-move forgery detection algorithms are weak, a detection algorithm based on exponential Fourier moments is proposed in this paper. Method First, a grayscale image is divided into multiple overlapping blocks. Then, the exponential Fourier moments of every block will be regard as a feature vector, and all vectors are sorted by lexicographic sorting. The questionable blocks are selected based on vector similarity and block displacement. Finally, the error similar blocks are removed by the neighbors' number and the angles' variance to locate the final tampered region. If an RGB image is detected, then each color channel can be independently processed to obtain three results, and the final result is obtained by performing an “and” operation. Result Most existing copy-move forgery detection methods usually convert an RGB image into a grayscale image, thereby leading to information loss. As a result, we detect each color channel to have three independent outcomes and integrate the different results. To make the method more robust, we use the consistency of the pasted region to remove error similar blocks. Experimental results show that the method is robust against post-processing operations, such as rotation, noise addition, and Gaussian blur. Compared with the method that uses radial harmonic Fourier moments, our proposed method has a better efficiency when the noise is added to the image. The detection rate increased by about 26.66%, and the error rate decreased by about 33.77%. Conclusion A user can easily creative a convincing image by copying and pasting content within the same image. The proposed method can detect and locate the duplicated regions. Moreover, the method is still effective even when an image is distorted by rotation, additive noise, or Gaussian blurring.