基于双通道R-FCN的图像篡改检测模型

随着大数据时代的到来和图像编辑软件的发展,恶意篡改图片的数量出现井喷式增长,为了确保图像的真实性,众多学者基于深度学习和图像处理技术提出了多种图像篡改检测算法.然而,当前提出的绝大多数方法在面对大量图片的情况下,篡改检测速率较低且小面积篡改区域检测效果较差.为了有效解决这些问题,本文首次将基于区域的全卷积网络(Region-based Fully Convolutional Networks,R-FCN)引入双通道篡改检测网络,通过彩色图像通道提取图像的表层特征,使用隐写分析通道挖掘图像内部的统计特征,并利用双线性池化层将两个通道的信息融合,构建了一种面向实际应用场景的图像篡改检测模型.其中,利用R-FCN中位置敏感得分图提高图像篡改检测效率,使用双线性插值算法提高小面积篡改区域的检测率.通过在国际主流的标准图像篡改数据集上进行实验,有效地验证了该模型的图像篡改检测速率相比当前最新模型提高2.25倍,检测精度提升1.13%到3.21%,本文提出的模型是一种更加高效而精准的图像篡改检测模型.

Dual-Channel R-FCN Model for Image Forgery Detection

With the explosive growth of malicious tampering images,many scholars have proposed multiple image forgery detection algorithms based on deep learning and image processing technologies.Although these algorithms have achieved good results,most of them have strong limitations in practical application.In order to solve this problem,we proposed a dual-channel forgery detection model empowered by Region-based Full Convolution Network(R-FCN),which was inspired by the two-stream network.The model included two parts:RGB channel and steganalysis channel.The design of dual channel enables the model to capture more features in the image and obtain a better detection effect.First of all,the model utilized the properties of each channel to extract the image’s features.The RGB channel captured the boundary,color,texture and other surface features of the image,and analyzed the tampering artifacts which were left by image forgery.Steganalysis channel used the Spatial Rich Model(SRM) filter layer to extract the residual noise of the image,and analyzed the inconsistency between the real area and the tampering area.Then,the model used the Region Proposal Network(RPN) to obtain the corresponding Region of Interest(ROI)location information from the feature maps,and combined the position-sensitive ROI pooling operations to get the score maps.Finally,the model used the bilinear pooling layer to fuse the information of the two channels,and processed the relevant features to obtain the corresponding category information and location information,so as to located the tampering area.On the one hand,the proposed model uses the design of the position-sensitive score map in R-FCN,which increases the number of shared computing network layers by changing the location of the ROI pooling layer,and improves the detection efficiency.On the other hand,bilinear interpolation is used to adjusting the output size of the feature map in the feature extraction network,which alleviates the weak expression ability of model features caused by convolution operation in the feature extraction process,and improves the detection accuracy of the small tampering area.Since there was not enough data in the standard dataset to train the neural network,we pre-trained our model on the synthetic dataset.We compared our model to four state-of-the-art models on three benchmark datasets,NIST,CASIA2.0 and Columbia.The comparison models were mainly divided into two categories:one traditional image forgery detection algorithm(CFA1) and three deep learning image forgery detection algorithms(Tam-D,J-Conv-LSTM and RGB-N).We have conducted a number of experiments to verify the performance of our model.The experimental results show that the dual-channel structure and bilinear pooling layer of the model improve the detection accuracy.In order to explore the superior performance of the model,we evaluate the model with three evaluation indexes:Average precision(AP),Fl-score and Frames Per Second(Fps).The evaluation results show that the image tamper detection rate of this model is 2.25 times higher than the current latest model,and the detection accuracy is increased by 1.13% to3.21%,verifying our proposed image forgery detection model is more efficient and accurate.