基于阴影检测模型的图像拼接盲取证

拼接篡改是最常见的篡改手段之一。在拼接带有阴影的图像时，篡改者为了掩饰篡改痕迹往往会伪造拼接物的阴影。提出一种基于阴影模型的拼接图像盲取证方法。分析各种经典的阴影模型，在此基础上建立一种适用于拼接篡改取证的阴影模型。使用图像区域增长算法实现对图像阴影的自动提取和定位，并采用能量最小化方法和干扰点去除技术得到鲁棒的阴影比例因子，利用阴影比例因子的不一致性对拼接篡改图像进行盲取证。实验结果表明，该拼接取证方法对于带阴影的拼接篡改图像有较高的检测准确率，能应对各种伪造阴影手段。

Blind Detection of Image Splicing Based on Shadow Model

Image splicing is a common type of forgery that takes some objects from other images. Splicing is hard to be detected for lack of references. When the objects have shadows, fake shadows are often created to conceal the tampering. A blind forensic method to detect spliced image based on a shadow model is proposed. The method is based on the fact that a natural shadow contains certain features absent in a fake shadow. A shadow model suitable for image forensics is established. Shadows are extracted and located by using an algorithm of image region dilation. A robust shadow scale factor is then obtained by using an energy minimization method, and interfering points removed. Thus, fake shadows can be determined from abnormal values of the shadow scale factor. Experiment results show effectiveness of the proposed method.