基于深度特征提取和DCT变换的图像复制粘贴篡改检测

针对图像复制粘贴篡改检测中算法时间复杂度过高和定位区域不完整的问题，提出一种基于深度特征提取和离散余弦变换的图像复制粘贴篡改检测算法。首先，融合图像颜色和纹理信息获得四通道图像，计算自适应特征提取阈值，并通过基于全卷积神经网络的特征检测器提取图像深度特征；其次，通过离散余弦变换提取块特征进行初步匹配，再利用点特征向量消除误匹配；最后，通过卷积运算精确定位篡改区域。通过在公共数据集上进行验证，充分展示了该算法在检测效率和定位区域完整性方面的优势。

Image copy-move forgery detection based ondeep feature extraction and DCT transform

Aiming at the problems of high algorithm time complexity and incomplete location region in image copy-move forgery detection, an image copy-move forgery detection algorithm based on deep features extraction and discrete cosine transform is proposed. Firstly, the color and texture information of the image are fused to obtain the four-channel image, the adaptive feature extraction threshold is calculated, and the depth feature is extracted by the feature detector based on the full convolutional neural network. Secondly, the discrete cosine transform is used to extract block features for preliminary matching, and point feature vectors are adopted to eliminate mismatches. Finally, the tampered regions are accurately located by convolution operation. The verification on the public dataset fully demonstrates the advantages of the algorithm in the detection efficiency and the integrity of the positioning area.