基于非局部自相关的复制粘贴检测算法  

Copy-Move Forgery Detection Algorithm Based on Non-Local Self-Correlation

作　　者：吴旭 刘翔[1] WU Xu;LIU Xiang(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《电子科技》2022年第10期59-64,共6页Electronic Science and Technology

基　　金：国家自然科学基金(81101105);文化部科技创新项目(2015KJCXXM19)。

摘　　要：针对数字图像复制-粘贴篡改无法区分目标与来源的问题,文中通过改进相似度匹配算法和利用非局部自注意力机制,在定位出篡改区域的前提下,解决了篡改源区域和目标区域的分类问题。总体框架为双分支检测网络,主分支采用经典U-net分割篡改区域像素,副分支通过孪生网络进行特征提取并计算自相关性,从而分割出篡改目标与源区域像素。将双分支融合后进行端到端训练,最终网络预测出三分类结果。实验结果表明,文中算法检测定位目标区域时的像素级分类精确率达到了80.47%,且F1值及准确度均优于对比算法。可视化结果和鲁棒性实验也表明文中算法具有良好的泛化性能。On account of the problem that forgery target and source of digital image copy-move manipulation cannot be distinguished,this study improves the similarity matching algorithm and uses non-local self-attention mechanism to solve the classification problem of copy-move forgery source and target areas,under the premise that manipulated regions are detected.The overall framework is a dual-branch detection network.The main branch uses the classic U-net to segment the pixel of forgery regions,and the auxiliary branch uses the siamese network to extract features and calculate the autocorrelation to separate the forgery targets and source area pixels.Finally,three-categories results can be predicted by end-to-end training after fusing two branches.The experiment result shows that the pixel-level classification accuracy of the proposed algorithm when detecting the localized target area reaches 80.47%,and the F1 value and accuracy are better than the compared algorithm.The visualization results and robustness experiments also show that the proposed algorithm has excellent generalization performance.

关 键 词：孪生网络 图像被动取证 复制粘贴篡改 相似度距离 非局部算子 自相关性 编码器-解码器 端到端 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN99[自动化与计算机技术—计算机科学与技术]