## 3 Experimental Results and Analysis
### Results
#### 3.3.2 Second Group of Experiments (Images with Combined Attacks)  
The aim of this group of experiments is to analyze the effects of several attacks on the performance of the proposed image forgery detection approach. In these experiments, a combination of attacks is applied to the images. For example, a combination of (forgery + compression), (forgery + noise), (forgery + scaling) or (forgery + rotation) is used to simulate the real attacks on the digital images, as shown below in Figure 7.  
It is also worth mentioning that, in this figure, the original watermark is inserted along with the attacked forged images to show the effects of forgery and attacks on the original watermark. Tables 2-5 show the results of the application of image processing and geometrical attacks to the forged images in the CASIA V1.0 dataset. The compression, noise,  
Figure 7: Examples of forged images attacked by some known attacks. rotation and scaling attacks are the most important attacks that can confront the digital image during the acquisition, processing or/and transmission. Table 2, below, shows the results in terms of precision, recall and F1 score classification measures, and NC, TAF, BER, PSNR and SSIM image quality measures after the attacking of the forged images by the compression attack. The results in Table 2 show that the precision, recall and F1 score values of the forgery classification measures increased ascendingly with the increasing value of the compression quality factor (i.e., the increase in the quality of the forged image) with average metric values of 97%, 97% and 97%, respectively. This is an expected result, as the increase in the quality of the compression produces high-quality images that lead ultimately to good classification results. The results in this table also show that the NC, TAF, PSNR and SSIM metric values increase gradually with the increasing value of the compression quality factor, with average metric values of 97%, 94%, 12.48% and 64%, respectively. Such behavior can be also attributed to the production of high-quality images after high-quality compression. For BER, we noticed that the error rates decreased with the increased compression quality of the forged image, which eventually implies good classification and reconstruction performance results. The noise in the acquisition sensor or during the data transmission has a non-trivial influence on the performance of the image forgery detection system. We observed from the experimental results in Table 3 that the precision, recall and F1 score classification measure values started to decrease gradually with the increasing noise level, with average values of 95% for all of the forgery classification metrics. Such behavior is expected, as noise decreases the quality of the forged images, which eventually affects the quality of the image features and texture. Then, the classification performance of the proposed approach will be affected by such a decrease in the quality of the features. We found the same trend in the NC (94%), TAF (94%), PSNR (12.10%) and SSIM (61%) image quality measures, i.e., the values of these measures decreased gradually with the increased noise level. However, the values of all of the explained measures were still good enough to resist the noise attack. Lastly, the error rates (BER) increased gradually with the increasing noise level, with an average error rate of 6%. For the scaling geometrical attack, the average values of the precision, recall and F1 score measure values were 93%, 95% and 94%, respectively, as shown in Table 4. From these results, we can notice that the scaling attack has a non-trivial influence on the forgery classification performance of the proposed approach, especially with the increasing value of the scaling factor. Furthermore, the values of the NC, TAF, PSNR and SSIM watermark image quality measures decreased gradually with the increasing value of the scaling factor, with average values of 92%, 92%, 11.02% and 57% for these metrics, respectively.