## 4 Experiments and Results  
The original image is as shown in Figure 4a. The binary logo watermark image watermarked into the inner part is shown in Figure 4b. In Figure 4c, a watermarked image with a proving ownership and authentication watermark is shown. The PSNR of the image is 40.577. Although the PSNR value may be seen as a lower fidelity value, the sacrifice is with semi-fragility and it pays off. The success of the algorithm for different types of attacks is given in Table 1. The algorithm authenticates an image that is Lossy-JPEG-compressed with a quality factor of 75%; the authenticated image is seen in Figure 5a. It shows only six blocks as changed which are not contiguous and can be considered an innocent type of operation. The algorithm does not authenticate 50% quality and 25% quality JPEG compressed images, as seen in Figure 5b,c. The algorithm also authenticates images with intensity adjustment and histogram equalization, as seen in Figure 6b,c. The most common objective evaluation tool, the mean square error (MSE), is very unreliable, resulting in poor correlation with the human visual system (HVS). The peak signal-to-noise ratio (PSNR) isone of the image-quality measurement metrics. It represents a ratio between the highest value of a signal and the power of distorted noise. In Figure 7a, the watermarked image has been tampered with as a non-existent ornament is placed on the mosque on the figure. In Figure 7b, it can be seen that the changed blocks are successfully signed as \"changed\" by the algorithm without any false negatives. The authenticator program needs the PSNR and the scrambling key for authentication. In Figure 7c, the watermarked image with no attack is attempted to be authenticated with the wrong key and almost all the blocks are signed as tampered with by the algorithm. If the wrong key is given to the authenticator, or the key is not known, the image cannot be authenticated. The algorithm does not authenticate watermarked images with scale attacks, which can be seen as an innocent type of operation. Moreover, the number of blocks marked as tampered with is largely as predicted for a watermarked image filtered by a 3 \(\times\) 3 averaging filter, as seen in Figure 8a. The Gaussian filtered image is marked tampered with for a very few blocks, as seen in Figure 8b. Figure 6 demonstrates watermarked images after re-scale authentication, intensity adjustment and histogram equalization attacks. There are several measurement metrics in the watermarking process. In semi-fragile watermarking, we use the similarity ratio, which measures the quality of the extracted secret information. Table 2 shows the SR values for the watermarked image and the extracted watermark. Table 3 shows a comparison with four of the previous approaches that resemble our study. When previous works are examined, some of the methods divide the host image into blocks and authenticate the blocks rather than the whole image. Most of the block-wise methods use a secret key to pair the blocks and calculate some value from one block and embed it into the corresponding pair. When compared with semi-fragile works [36], a watermarked image compressed with 70% quality is authenticated JPEG, and does not mention another type of image operation that the authentication watermark survives. Our method authenticates an image that is subjected to 75% JPEG compression, histogram equalization and gamma correction. [PERSON] and [PERSON]'s [27] authentication algorithm can resist 90% JPEG compression at most. In these respects, our algorithm performs better. The algorithm may detect some changes in some block pairs in innocent operations of a 3 \(\times\) 3 average filter, scale-rescale operation, JPEG 50% compressions, and JPEG 25% compressions. Thus, selective fragility is not successful for this kind of innocent operation.