**5. Experimental Results and Analysis**

We conducted a series of experiments to verify the robustness of the algorithm. Robustness refers to the ability to detect the watermark after the designated class of transformations [53]. Bit Error Ratio (BER) is a commonly used metrics to measure the robustness of watermarking methods. BER is defined as:

$$\text{BER}(W, w) = \frac{n\_{\mathcal{C}}}{l} \tag{21}$$

where *ne* is the number of erroneous bits. A lower BER indicates the extracted results are closer to the original watermark information, which means the better robustness. Since the threshold *T* for watermark detection is set to 8, and the watermark length *l* is set to 60 in our method, this means that watermark detection is successful when BER is below 0.1333.

In Section 5.1, the robustness to common image attacks is discussed. In Section 5.2, the proposed scheme is compared with two state-of-the-art schemes and the performance against screen-cam attack is analyzed in detail. In Section 5.3, considering real-life scenarios, some hypothetical scenarios were designed to verify the robustness of the algorithm.

The experimental instruments are as follows: The display device in this scenario is a 23-inch monitor with 1920 × 1080 pixels. Since the ordinary users' monitors are not accurately corrected, to mimic a real-world scenario, the monitors are not explicitly calibrated. An iPhone X with dual 12 MP pixels is used as the photography equipment. The lens is well focused while shooting, and shooting quality is controlled as much as possible.

The host images are the eight images in Figure 2. The PSNR values of each square region that contains an LSFR are controlled to be no less than 39 dB in our experiment. Figure 14 shows the corresponding watermarked images generated by the proposed method.

**Figure 14.** Watermarked images.
