# Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization
## 3 Method
### Scale-Adaptive Correlation Computation
Then find the target point in the region by another linear transformation:

\[z_{c}^{(k)}=\mathbf{F}_{sq}(\vec{f}_{c}^{(k)})=\frac{1}{H\times W}\sum_{i=1}^{H} \sum_{j=1}^{W}\vec{f}_{c}^{(k)}(i,j) \tag{3}\]3

Third, fuse them by taking the average response across scales for each position separately. Finally, a new score map is got. The discussion in the experimental section shows that the pyramid version with scale = {384, 512, 640} achieves the best performance. ## 4 Experiment

In this section, we demonstrate the superiority of our model from an experimental point of view. In the task of CISDL, the most challenging are (1) processing arbitrary-sized images; (2) finding spliced regions as well as pinpointing their exact boundaries under various transformations; and (3) processing the multiscale object in the same image. Based on the above challenges, the enhanced effect of the model on tampering area localization and detection was verified by visual comparisons and quantitative results for the three improvements. ### Benchmark Datasets and Compared Methods

To evaluate the effectiveness of SADM and PSADM, we conducted localization and detection experiments of tampered regions. According to the characteristics of each dataset, we evaluate the localization performance on the generated dataset from MS COCO and verify that SADM outperforms all the previous approaches. Then, the paired CASIA dataset and Media Forensics Challenge 2018 (MFC2018) dataset are tested to demonstrate the superiority of the proposed model in terms of detection performance. (1) The generated datasets from MS COCO. The MS COCO dataset consists of 82,783 training images and 40,504 testing images. It provides object annotations for abundant images and can generate accurate ground truth masks by the enormous number of training pairs, which is suitable for localization performance experiments. After synthesis of the MS COCO dataset, thete generated dataset's training sets consist of 1035,255 training pairs, of which one-third were foreground pairs, one-third were background pairs, and one-third were negative sample pairs. The generated dataset's test sets were divided into three main groups, namely the Difficult set (1-10%), the Normal set (10-25%) and the Easy set (25-50%). We adopt the pixel-level IoU (Intersection over Union), NMM (Nimble Mask Metric) and MCC (Matthews Correlation Coefficient) of the tampered regions and the average indicator of all the tested image pairs to evaluate the localization performance. (2) The paired CASIA dataset. The CASIA TIDEv2.0 dataset is initially designed for the image of copy-move and the classic splicing detection problem [6]. The new paired CASIA dataset we used consists of 3642 positive samples and 5000 negative samples selected from the CASIA TIDEv2.0 dataset by CISDL. Due to the lack of ground truth masks, this new paired CASIA dataset only focuses on estimating the splicing detection performance [6]. We adopt F1-score, precision and recall to evaluate the detection performance. (3) MFC2018. The Media Forensics Challenge 2018 (MFC2018) dataset has 16,673 negative image pairs and 1327 positive image pairs. MFC2018 is a challenging dataset, collected particularly for two problems of the CISDL task: quantitative evaluation of detection performance and localization performance, using a large number of negative image pairs and ground truth by visual comparison. We use AUC, an official metric, to quantify the capability to distinguish between the two categories and employ the EER (Equal Error Rate) score to evaluate false-alarm images. ### Training Procedure and Testing Set

This model is trained with a \(1\times 10^{-6}\) base learning rate for 3 epochs and 18 samples for each batch size. To calculate the model loss, we use the BCEWithLogitsLoss function. Meanwhile, we employ the Adadelta optimizer to optimize model parameters. Additionally, our feature extractor uses a migration learning approach by using a pre-trained model of VGG16 [10].