Abstract:

Several discriminative problems exist in copy-move forgery detection, including the difficulty of using keypoints to cover smooth regions, lack of color descriptive capability in feature representations, and insufficient accuracy of feature matching. This study proposes a highly discriminative image copy-move forgery detection method. For keypoint extraction, an image is divided into different super-pixel regions according to texture, and the keypoints are adaptively extracted from such regions, where the keypoints uniformly cover the smooth regions. For feature representation, a quaternion-based feature description method is proposed that can accurately describe the color information of an image. For feature matching, a Reversed generalized 2 Nearest Neighbors (Rg2NN) matching algorithm is used to improve the matching accuracy of multiple keypoints. During post-processing, the detection results are obtained using a fast mean-residual normalized production correlation (NNPROD) algorithm. Experimental results demonstrate that the proposed algorithm achieves excellent accuracy across multiple benchmarks and is robust against common geometric and signaling attacks.

Key words: copy-move, forgery detection, super-pixel, color, discriminative

摘要：

针对复制-粘贴篡改检测的判别性问题，包括特征点难以覆盖图像平滑区域、特征表示不具备彩色图像描述能力以及特征匹配不够精确，给出一种高判别性的图像复制-粘贴篡改检测方法。在特征点提取环节，根据纹理程度将图像分成不同超像素区域，并在不同区域自适应地提取图像特征点，从而使特征点均匀地覆盖图像平滑区域。在特征表示环节，提出基于四元数的特征描述方法，以更好地描述图像的色彩信息。在特征匹配环节，使用一种新型的逆序广义2近邻(Rg2NN)匹配算法，提高多特征点的匹配精度。在后处理环节，使用快速去均值归一化积相关(NNPROD)算法进行相关性检查，得到检测结果。实验结果表明，所提方法在多个基准上实现了先进的综合检测精度，并且对常见的几何和信号攻击鲁棒。

关键词: 复制-粘贴, 篡改检测, 超像素, 颜色, 判别性