# 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
Figure 8 presents the experimental results for these scaling factors, from left to right. The first row represents the original image, the second row shows the binary ground truth provided in the dataset, and the third row displays the experimental results of the proposed method. By comparing the detection results in the figure (third row) with the binary images provided in the dataset (second row), it is clear that the proposed method still has good detection performance in scaling forgery detection. Especially for small compression factors, the detection results almost have no missed detection areas. However, as the scaling factor increases, for example, at a scaling factor of 1.5, it can be observed that there are missed detection areas in the boundary of the detection results. In summary, after conducting experiments on simple translation forgery, rotation forgery at different angles, and compression factor forgery, it can be seen by comparison with the binary images provided in the dataset that the method proposed in this paper is an effective detection algorithm. For geometric transformation forgery, the proposed method can accurately locate tampered regions, especially in smooth areas, where the detection performance still shows good results. In addition, it also performs well in small-scale rotation and compression tampering scenarios. In addition to using intuitive effect diagrams to demonstrate the effectiveness of the proposed method, we also conducted experiments at the image level. All images from the Ardizzone and CoMoFoD datasets were tested to calculate the number of tampered images correctly identified as tampered, the number of tampered images incorrectly identified as real, and the number of real images incorrectly identified as tampered. In comparison with other methods in the literature, precision, recall, and \(F_{1}\) were calculated, and the experimental results are listed in Tables 2 and 3. The standard deviations of the three evaluation metrics were also calculated, and the data are presented in Table 4.  
Figure 8: Different scaling factor transformations falsify experimental results. **First row**: original images; **second row**: binarized truth images; **third row**: effect images of forgery detection. We comprehensively analyzed the performance of the proposed algorithm through three evaluation metrics and compared it with the algorithms proposed in [17; 38]. Table 2 presents the experimental data obtained by testing the proposed method and the comparison algorithms on the Ardizzone dataset [37], where it can be clearly seen that the method has certain advantages. Compared with the other algorithms, the precision of our proposed algorithm is higher. Although the recall rate is slightly lower than that of the algorithm in [17], the \(F_{1}\) value of the proposed algorithm indicates a significant advantage. This is mainly because in the feature extraction stage, we used two detection methods, and the A-KAE detector extracted more features in smooth areas, resulting in better detection performance in the tampering localization stage. Table 3 shows the results of experimental testing on the CoMoFoD dataset. The proposed algorithm has significant advantages in precision, which is higher than that of the two comparison algorithms. The proposed method also has significant advantages in recall and overall \(F_{1}\) value. The main reason for this is that the method proposed in [17] only uses SURF for feature point extraction, determines suspicious areas by replacing matching feature points with corresponding superpixel blocks, and then merges adjacent blocks based on similar local color features. This method is less robust to rotation and scale invariance and does not perform well in geometric transformation forgery detection. In this paper, we combined SURF and A-KAE in the feature extraction stage to obtain enough feature points in smooth areas, and removed mismatches through a density-based clustering method, thereby effectively and accurately locating tampered areas.