# Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization
## 3 Method
### Scale-Adaptive Correlation Computation
Since the scale of objects in the image is arbitrary, a fixed size perceptual field will limit the localization and detection effect, and ability to recognize object boundaries. To address these problems, we use diverse groups of different scale images to find the best formal of our model. The scale of each input image is experimented with [384; 512; 640], [448; 576], respectively, called as PSADM. We adopt postfix to annotate different strategies, such as \" [384; 512; 640]\" denotes the rescaled version of original images, and the size of original images are identified by \"256/384/512\". Experiments demonstrate that the localization performance is improved to a new level after adopting this strategy. ### Experimental Results

#### 4.3.1 The Generated Datasets from MS COCO

(1) Parametric test. **Truncation error. ** We adopted a truncation operation in the scale-adaptive correlation calculation module to process arbitrary-sized images. After sorting the response values for the feature maps generated from SE-blocks, we tested the response values for the first 32, 128 and 256 channels. The test results are shown in \[z_{c}^{(k)}=\mathbf{F}_{sq}(\vec{f}_{c}^{(k)})=\frac{1}{H\times W}\sum_{i=1}^{H} \sum_{j=1}^{W}\vec{f}_{c}^{(k)}(i,j) \tag{3}\]4 The model has the best localization performance when the response values of the first 32 channels are taken. As the response value decreases, the feature it represents becomes weaker. Therefore taking too many of these response values is not conducive to feature extraction. compared with previous models such as AttentionDM. In [7], [PERSON] et al. employed a sliding window strategy to compensate for the defect of only being able to handle fixed-size images. Yet, it consumes more computing time. To compare with the sliding window strategy, we evaluate the effectiveness of the scale-adaptive correlation computation module on the same dataset. The results of handling 256 \(\times\) 256, 384 \(\times\) 384, 512 \(\times\) 512 are shown in \[z_{c}^{(k)}=\mathbf{F}_{sq}(\vec{f}_{c}^{(k)})=\frac{1}{H\times W}\sum_{i=1}^{H} \sum_{j=1}^{W}\vec{f}_{c}^{(k)}(i,j) \tag{3}\]5 As we can see, it is obvious that IoU, MCC and NMM are rising, which thoroughly explains the strong ability of SADM to process arbitrary-sized images. To further improve the capability of processing large-scale images, many kinds of parameters of the PSADM have been experimented with, as shown in \[z_{c}^{(k)}=\mathbf{F}_{sq}(\vec{f}_{c}^{(k)})=\frac{1}{H\times W}\sum_{i=1}^{H} \sum_{j=1}^{W}\vec{f}_{c}^{(k)}(i,j) \tag{3}\]6 Under various considerations, the PSADM-512-[384, 512, 640] achieves superior performance on recognizing small regions and pinpointing their exact outline. And then, we will test the detection ability of this version. (3) Complexity analyses. Table 4 lists the testing time, parameters numbers and implemented frameworks. All experiments were conducted on a machine with Intel(R) Core (TM) i7-5930K CPU @ 3.50 GHz, 64 GB RAM and a single GPU (TITAN X). As shown in Table 4, the number of the trainable parameters for SADM is 15,407,276, slightly more than the number of parameters for DMAC but less than AttentionDM. Although the testing time of SADM is 0.0298 s, marginally lower than that of DMAC, it achieves significant improvements in localization and detection. forged probability. As shown in Table 5, SADM has a very high Precision (nearly 100%) and a little low Recall value. For the size of 256 \(\times\) 256, SADM improves the precision from 92.88% to 99.01%, while the recall score only decreases by 3.87%. F1-score has been further improved, which is a good tradeoff between Precision and Recall. As for the large-size such as 512 \(\times\) 512, these indicators are further enhanced. In addition, the detection performance of large-scale images is further enhanced by the processing of PSADM. Visual comparisons, afforded in Figure 7, show that SADM achieves very good performance. It has a high level of competence in detecting small regions and accurate boundaries.