# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Copy-Move Forgery Detection
#### 5.4.1 Geometric Transformation Forgery Detection  
This paper mainly focuses on the experimentation and testing of geometric transformation forgery, including simple translation forgery detection, rotation forgery detection and scaling forgery detection. A certain number of photos were selected from the dataset for each type of tampering, and the experimental detection results are presented in the form of detection effect images. Simple translation forgery detection: We conducted experimental tests on geometric transformation forgery, including simple translation forgery detection, rotation forgery detection, and scaling forgery detection. For each type of tampering, a certain number of photos were selected from the dataset for experiments, and the results of the experimental detection are displayed. Partial experimental results are given below, and are shown in Figure 6. The first row of images in the figure represent the original image, the second row comprises the binarized truth images provided in the dataset (used for comparison with the detection results), and the third row presents the effect images. The third row shows the detection results of the proposed algorithm, which was mainly used to test for simple translation forgery operations. From Figure 6, it can be intuitively seen that the experimental effect image of the proposed method almost coincides with the binary image given in the dataset, and there are no missed detection areas. For images that have not undergone complex tampering, the proposed method can more accurately locate tampered areas, and there is no occurrence of false detection or missed detection in simple tampering. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Row** & **First Row** & **Second Row** & **Third Row** & **Fourth Row** \\ \hline SIFT & 0 & 16 & 215 & 16 \\ SURF & 0 & 2 & 124 & 5 \\ A-KAZE & 0 & 23 & 175 & 5 \\ BRISK & 0 & 17 & 96 & 4 \\ SURF + A-KAZE & 98 & 145 & 325 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of matching points of different feature points in tampered area. Figure 5: Experimental photos for detecting the number of matching points of different key point features in the tampered area: (**a**) obscured images; (**b**) tampered images; (**c**) obscured images; (**d**) tampered images. Rotation transform forgery detection: We conducted forgery detection experiments on images from two publicly available datasets, with the images subjected to rotation-based tampering at angles of \(\pm 1^{\circ}\), \(\pm 2^{\circ}\), \(\pm 3^{\circ}\), \(\pm 4^{\circ}\), \(\pm 5^{\circ}\), and \(\pm 10^{\circ}\). The images in Figure 7 demonstrate the detection results for rotation angles of \(1^{\circ},3^{\circ},5^{\circ}\), and \(10^{\circ}\). The first row of the figure shows the original images, the second row shows the binarized truth images provided in the dataset (used for comparison with the detection algorithm), and the third row shows the detection results of the method proposed in this paper; the detection results are shown at different rotation angles. The comparison with the binarized truth images from the dataset indicates that the proposed method maintains high detection accuracy even at different rotation angles. The comparison with the binarized truth images in the second row shows that there are almost no missed detections. This advantage is particularly evident in small-scale rotations, and the proposed method has good detection performance. Figure 6: Experimental results of simple translational forgery detection. **First row**: original images; **second row**: binarized truth images; **third row**: effect images of forgery detection. Figure 7: Rotation angle forgery detection experiment results. **First row**: original images; **second row**: binarized truth images; **third row**: effect images of forgery detection. Scale transform forgery detection: We conducted scale manipulation experiments on images from two publicly available datasets. The scaling factors used were 0.9, 0.95, 1.05, 1.25, and 1.5.