Adaptive residual algorithm for image splicing detection
Objective In recent years,digital media have become central to the exchange of information in our daily lives.With the rapid development of image editing tools and deep learning techniques,tampering with transmitted images is easy.Image splicing is one of the most common types of image tampering.Malicious image splicing challenges reputation,law,and politics.Therefore,various approaches have been proposed for detecting image splicing forgeries.Deep learning has also been successfully applied in image splicing detection.However,the existing deep learning-based works usually preprocess the input images by extracting features filtered by the high-pass filters with fixed parameters,which does not consider the differences between images.Method Therefore,a new image splicing detection algorithm is proposed in this paper.First,an adaptive residual module(ARM)is designed to highlight the splicing traces.In the ARM,the residual after the convolution operation is serialized several times,and the attention mechanism is used to realize the nonlinear inter-action between channels after each connection.Unlike ordinary filters with fixed parameters,the ARM module entirely relies on the feature reuse and attention mechanism of residuals to retain and enlarge the details of the splicing.Then,a squeeze and excitation(SE)module is used to reduce the inter channel information redundancy generated by ARM residual feature extraction.The SE module uses an average adaptive pool to generate channel statistics information on global space and the gating mechanism of the Sigmoid activation function to learn channel weights from channel dependencies.Finally,a new image splicing detection algorithm is proposed by combining with the proposed ARM and the backbone network Eff-cientNet,a model with excellent performance in image classification.Result Experimental results show the proposed algo-rithm achieves 98.95％,98.88％,100％,100％,and 88.20％ detection accuracies on CASIA image tampering detection evaluation database(CASIA Ⅰ),CASIA Ⅱ,COLUMBIA COLOR,NIST special database 16(NIST16),and FaceForen-sic++,respectively,and obtains higher accuracy than the existing algorithms.Moreover,the proposed ARM algorithm improves the accuracy of backbone network by 3.94％ on the CASIA Ⅱ dataset.Regarding the computational time,on the CASIA Ⅱ dataset,the training time per batch of the proposed algorithm is 71.75 s,and the test time for a single image is 0.011 s,which is less than the existing algorithms.In addition,the size of the parameters of ARM is 0.003 6 MB,which is about 2‰ of the parameters size of the backbone network EfficientNet,and the FLOPs are about 0.037 G.Conclusion This paper proposes an image splicing detection algorithm based on ARM,and the proposed algorithm performs well on five public datasets.The designed ARM is a plug-and-play lightweight,adaptive feature extraction module,and it can be migrated on other models,such as Xception and ResNet.
万方数据
维普
