## 5 Experimental Results  
The verification of the proposed algorithm is performed by measuring of Peak Signal to Noise Ratio (PSNR), which represents the visual quality of a watermarked image in relation to the total size of a watermark. Additionally, to consider the human visual system (HVS), Structural Similarity (SSIM) metric [35] and Universal Quality Image (UQI) index [36] are measured to assess the quality of the image with an embedded watermark. Furthermore, the bit error rate (BER) is also analyzed, for different lengths of the hidden message. The measurements of these ratios were performed for watermarks inserted in DCT and PHL transform domains so that the performance of both approaches may be compared. For test purposes, a random bit stream is used as a watermark message. The tests were carried out  
Figure 8: Result of image manipulation detection. (**a**) Tampered watermarked image. (**b**) Detected regions where the image has been manipulated.  
Figure 9: Result of image manipulation detection with greater precision. (**a**) Tampered watermarked image. (**b**) Detected regions with greater precision.  
in a MATLAB environment. The referenced DCT method originates from the one described in [3].  
For test purposes, 23 images from 'Images 4k' dataset [37] have been selected. The dataset contains 2057 files. The test images were selected in such a way that they represent different visual characteristics, i.e., low and high contrast and brightness as well as various color distributions. The dimensions of the images were reduced by half to 1920 \(\times\) 1080 size so that the calculations and the watermark embedding process are speeded up.  
The relation between the PSNR ratio and the length of a hidden bit stream is presented in Figure 10. It can be observed that a perceptual quality of an image with a watermark inserted in the PHL spectrum is consistently better than in the case of a watermark embedded in the DCT domain. It is assumed that the PSNR above 35 dB indicates that the two images being compared are visually identical, with no perceptual loss of quality [38]. Therefore, both techniques provide satisfying results as far as the imperceptibility of a watermark is concerned, for the size of a watermark exceeding even 100,000 bits.  
SSIM is a quality assessment metric based on the visual changes in local structure and contrast between two images. It provides a good approximation of human visual perception. The metric values can range from 0 to 1, where 1 indicates perfect similarity [35]. The relation between SSIM and the total size of a watermark is presented in Figure 11. The results measured for the PHL method are slightly better than the ones achieved in the DCT approach. However, both methods according to this metric provide satisfying results.  
UQI index is designed to model image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although it does not employ any human visual system model, it was proved to be consistent with subjective quality assessment [36]. UQI index can vary between \(-1\) and \(1\), where value 1 indicates no distortion present in the image. The relation between UQI and the length of a hidden message is presented in Figure 12.  
The relation between the BER ratio and the size of a watermark is shown in Figure 13. It can be noticed that both methods guarantee a low bit error rate (\(<\)0.1%) for the watermark size ranging from 5000 to 105,000 bits. Therefore, both solutions are useful when a limited, but still, in most applications, sufficient, amount of information needs to be hidden in an image.  
Figure 10: Relation between PSNR ratio and the watermark capacity (PHL vs. DCT).  
Figure 11: Relation between SSIM metric and the watermark capacity (PHL vs. DCT).  
Figure 12: Relation between UQI index and the watermark capacity (PHL vs. DCT).  
Figure 13: Relation between BER ratio and the watermark capacity (PHL vs. DCT).