# Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization
## 3 Method
### Scale-Adaptive Correlation Computation
Furthermore, it is also sensitive to the arbitrary-sized images as well as robust to transformation and rotation changes. #### 4.3.3 MFC2018

Since MFC2018 is definitely collected for the CISDL task, we compare SADM, PSADM with DMVN, DMAC and AttentionDM on MFC2018. The MFC2018 challenge provides the evaluation codes, AUC and EER scores, which are calculated and shown in Table 6[36]. Compared with AttentionDM[9], SADM achieves the lowest EER score for 256 \(\times\) 256 images with an AUC decreased by 0.014, which far exceeded the previous CISDL approach. As for large-scale images, SADM can also achieve higher AUC and lower EER. Additionally, PSADM further enhance the effect of processing large-scale images, with AUC increased by 0.0037 and EER decreased by 0.0019 based on \"SADM-512\". Figure 8 provides visual comparisons. Obviously, SADM has a better capability to detect small regions and accurate boundaries than previous methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AUC** & **EER** \\ \hline DMAC[7] & 0.7542 & 0.3123 \\ DMAC-adv[7] & 0.7511 & 0.3093 \\ AttentionDM[9] & 0.7922 & 0.2756 \\ \hline SADM-256 & 0.7782 & 0.2527 \\ SADM-384 & 0.7825 & 0.2490 \\ SADM-512 & 0.7945 & 0.2470 \\ PSADM-512-[384, 512, 640] & **0.7982** & **0.2489** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons on MFC2018. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** \\ \hline [PERSON] et al.[32] & 0.5164 & 0.8292 & 0.6364 \\ [PERSON] et al.[33] & 0.9969 & 0.5353 & 0.6966 \\ [PERSON] et al.[34] & 0.9614 & 0.5859 & 0.7309 \\ [PERSON] et al.[35] & 0.9897 & 0.6334 & 0.7725 \\ \hline DMVN-loc[6] & 0.9152 & 0.7918 & 0.8491 \\ DMVN-det[6] & 0.9415 & 0.7908 & 0.8596 \\ DMAC[7] & 0.9255 & 0.8668 & 0.8952 \\ DMAC-adv[7] & 0.9657 & 0.8576 & 0.9085 \\ AttentionDM[9] & 0.9288 & **0.9204** & 0.9246 \\ \hline SADM-256 & 0.9741 & 0.8758 & 0.9263 \\ SADM-384 & 0.9835 & 0.8771 & 0.9314 \\ SADM-512 & 0.9903 & 0.8778 & 0.9316 \\ PSADM-512-[384, 512, 640] & **0.9932** & 0.8924 & **0.9329** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons on the paired CASIA dataset. Figure 7: Visual comparisons on the paired CASIA dataset. ## 5 Conclusions

In this paper, we propose a Scale-Adaptive Deep Matching network (SADM) for CISDL, and a pyramid version of SADM (PSADM). SADM consists of three components: the feature extractor, the scale-adaptive correlation computation module and the mask generator. The correlation-computation with truncation operations are proposed to deal with arbitrary-sized images. The mask generator is designed to reconstruct the spatial information of an image and generate fine-grained mask without requiring more computational complexity. The PSADM is applied to improve the ability of multiscale object detection and matching. Experimental results show that the proposed method can achieve better performance than the state-of-the-art methods on the publicly available datasets. Figure 8: Visual comparisons on the MFC2018 dataset. **Author Contributions:** Conceptualization, S.X. and Y.L. ; methodology, S.L. and Y.L. ; software, S.L. and Y.L. ; validation, S.L. ; formal analysis, S.L., Y.L. and C.X. ; investigation, S.L. ; data curation, S.L. ; writing--original draft preparation, S.L. ; writing--review and editing, S.X., S.L., Y.L., C.X. and N.G. ; visualization, S.L. ; funding acquisition, Y.L. and C.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by National Natural Science Foundation of China grant number 62102010 and 62002003. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All data generated or analyzed during this study are included in this published article. **Acknowledgments:** This work was supported by NSFC under 62102010 and 62002003. **Conflicts of Interest:** The authors declare no conflict of interest.