Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN
Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN
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DOI：
10.3390/sym11101223
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发表时间：
2019-10-01
期刊：
影响因子：
2.7
通讯作者：
Sun, Shuifa
中科院分区：
文献类型：
--
作者：
Wei, Xiaoyan;Wu, Yirong;Sun, Shuifa
由于用于产生被操纵图像的工具的广泛可用性，大量的数字图像在各种媒体中被篡改，如报纸和社交网络，这使得检测被篡改的图像尤为重要。因此，本文提出了一种利用Faster Region-based Convolutional Neural Network （Faster R-CNN）模型与边缘检测相结合的图像处理检测算法。该算法首先将原始篡改图像及其检测到的边缘送入对称的ResNet101网络中提取篡改特征；然后，将这些特征放入感兴趣区域池化层。采用双线性插值法代替RoI最大池化法获得RoI区域。将原始输入图像和边缘特征图像的RoI特征送入双线性池化层进行特征融合后，在全连接层进行篡改分类。最后，利用区域建议网络（RPN）对伪造区域进行定位。在三种不同的图像处理数据集上的实验结果表明，该算法比现有的图像处理检测算法更有效地检测出篡改图像。
Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.