数字图像取证是计算机取证、信息安全领域的一门新学科。为实现照片图像与真实感计算机图形的可靠识别,提出一种基于图像稀疏表示的数字图像取证方法,该方法在抵抗压缩方面具有较好性能,从而保证图像压缩不会改变照片图像与真实感计算机图形的真实性本质。Tetrolet变换为保护图像局部几何结构,在L¹-范数最小约束下搜索4×4图像块的最优覆盖(Covering)形式,获得图像的稀疏表示。观察自适应值c的统计分布,得到一幅图像中117种Covering出现次数的归一化直方图,从而得到图像的HoC(histogram of covering)特征。实验结果表明,在饱和度(S)分量提取的HoC特征能够很好地刻画照片图像与真实感计算机图形在局部几何结构上的不同统计特性,算法在识别能力、泛化能力,尤其是抵抗压缩能力上表现出良好性能,能够应用于图像真实性检测及照片图像与计算机图形的自动分类。

Based on the sparse representation of images, a new approach to distinguish photographic images and photorealistic computer graphics is proposed. The proposed approach is robust enough to compression to guarantee image authenticity forensics. The Tetrolet transformation chooses the optimal tetromino partition for each 4×4 image block in terms of the minimal L¹-norm criterion to protect local image geometry structure and to obtain the sparsest image representation. When observing the adaptive values c, an image is represented as a normalized histogram with 117 bins corresponding to the number of occurrences of different block covering, i.e. the features of HoC (Histogram of Covering). The experimental results demonstrate the HoC features extracted from S (saturation) are able to characterize the distinct statistical properties in the local geometry between photographic images and photorealistic computer graphics. The proposed approach is applicable to image authenticity detection and auto-classification.