# Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization  
[PERSON]  
1 Beijing Electronic Science and Technology Institute, Beijing 100070, China; [EMAIL_ADDRESS] (S.X. );  
[EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (C.X. ); [EMAIL_ADDRESS] (N.G.) 1  
[PERSON]  
1 Beijing Electronic Science and Technology Institute, Beijing 100070, China; [EMAIL_ADDRESS] (S.X. );  
[EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (C.X. ); [EMAIL_ADDRESS] (N.G.) 1  
Footnote 1: [[https://www.mdpi.com/journals/applsci/article/pdf/100%](https://www.mdpi.com/journals/applsci/article/pdf/100%)]([https://www.mdpi.com/journals/applsci/article/pdf/100%](https://www.mdpi.com/journals/applsci/article/pdf/100%))  
network (FPLN), which can detect and localize small spliced regions by fusing pyramidal feature maps with different resolution [1]. However, the loss of spatial information limits the discriminative ability and localization accuracy of the splicing model. In [7], [PERSON] et al. proposed a new deep matching network for CISDL named DMAC, using atrous convolution to generate two high-quality candidate masks. Additionally, they employ a detection network and a discriminative network to optimize the pretrained DMAC further. Though this framework achieves remarkable improvement than DMVN, the discriminative ability of DMAC and DMVN for simple scalar product is still limited in correlation computation. [PERSON] et al. proposed an encoder-decoder architecture based on an attention-aware mechanism in [9], which is named as AttentionDM to mitigate these problems. AttentionDM generates fine-grained masks by building a decoder with atrous spatial pyramid pooling (ASPP) [10]. A channel attention block is added to the correlation computation module to emphasize channelwise informative features. However, AttentionDM can only process fixed-size images for the restriction of correlation computation module and cannot be applied to actual multiscale target detection. In a word, there are still some challenges hindering the development of CISDL: (1) fixed-size image processing; (2) degraded spatial information of extracted features; (3) multiscale objects. In this paper, we propose a novel Scale-Adaptive Deep Matching network (SADM), as shown in Figure 1. In the proposed method, we make three improvements: First, a scale-adaptive correlation computation module is proposed based on squeeze-and-excitation (SE) blocks [11] and truncation operations to deal with arbitrary-sized images. Second, an attention-based separable convolutional block is constructed in mask generator to recover spatial information further. The attention-based separable convolutional block is composed of depthwise separable convolution and spatial attention, that depthwise separable convolution can help to improve efficiency as well as reduce the model parameters. Meanwhile, spatial attention can help recover the spatial information. Last but not least, we propose a pyramid framework of SADM to address the problem of insufficient edge detection and multiscale object detection. In summary, our contributions are:  
* A novel scale-adaptive deep matching network (SADM) is constructed based on a newly designed scale-adaptive correlation computation module. * A novel mask generator is designed by attention-based separable convolutional blocks, consisting of depthwise separable convolution and spatial attention. This kind of  
Figure 1: Overview of the proposed SADM. The probe P and potential donor image D are input to the network. Three groups of feature maps with the same size are generated from the feature extractor. They are processed with the scale-adaptive correlation computation module composed of L2-Normalization, SE block, correlation-computation and ReLU. It generates two correlation maps, which are fed into the mask generator with attention-based separable convolutional blocks to generate two fine-grained masks \(\mathrm{P_{m}}\) and \(\mathrm{D_{m}}\).