## 3 Results and Discussion
The Four received stego images are given to the extraction algorithm along with the received keys, and black & white binary data images are extracted from the red plane, as shown in Figure 16a-d. The received black & white images contain noise due to the channel. These noises are filtered out using the mode filter, and the black & white binary data images are restored, as shown in Figure 17a-d. The restored image of size (960 \(\times\) 540) is resized into (1920 \(\times\) 1080), as shown in Figure 18a-d, to achieve the required data capacity. This process is repeated for green and blue planes respectively. Figure 14: Hough Line Detection, (*blue color—actual line present in the cover image, pink color—detected Hough lines, and green color—detected ROI). Figure 13: (**a**) Reference Image, (**b**) Captured Image, (**c**) Matched Features, (**d**) Valid Features, (**e**) Restored Image. Figure 16: Extracted data image (**a**) Image 1, (**b**) Image 2, (**c**) Image 3, (**d**) Image 4. Figure 17: _Cont._  
Figure 15: Received Stego Image(**a**) Image 1, (**b**) Image 2, (**c**) Image 3, (**d**) Image 4. **Complexity Estimation:**  
In the proposed algorithm, the 960 \(\times\) 540 HH subband of the cover image is divided into 4 \(\times\) 4 blocks. Therefore, the total number of blocks is 32,400. Each block is selected randomly until every LL block of the data image is embedded within HH band of the cover image. The selection blocks can be done in 32,400! ways. In each block, eight different patterns are applied, and the pattern with the least MSE is selected for that block. This can be done in 8 ways. Therefore, for a brute force attack, it takes 32,400! \(\times\) 8 ways to break the security of the proposed algorithm. **Accuracy and Bit Rate:**  
At the receiver, the accuracy of the proposed system is estimated for all four images using both the proposed and pre-existing embedding algorithms. The results are shown in Table 4. It was observed that the proposed embedding algorithm produces an accuracy of 97.6%, which is significantly higher than that of existing algorithms. \[\text{Accuracy}(\%)\ =\ \left(\ \frac{\text{Total Number of Blocks }-\text{Number of Erroneous Blocks}}{\text{Total Number of Blocks}}\ \right)\times 100 \tag{12}\]  
Bit Rate = (Number of Blocks \(\times\) Number of Frame \(\times\) Number of Planes)  
Figure 17: Restored Black &White Data Image (**a**) Image 1, (**b**) Image2, (**c**) Image 3, (**d**) Image 4. Figure 14.0 Resized Black& White Data Image (**a**) Image 1, (**b**) Image 2, (**c**) Image 3, and (**d**) Image 4. Furthermore, the accuracy and bit rate for different n \(\times\) n blocks are compared for 1920 \(\times\) 1080 images, as shown in Table 5 using Equations (12) and (13). It was observed that for n=16 the accuracy was high but the bit rate was low, and for \(n\) = 4, the accuracy was low but the bit rate was high. Therefore, it is suggested that \(n\) = 8 be used, which provides good accuracy and bit rate. The time complexity of the proposed algorithm is estimated for four cover images in the following scenario. Three pieces of command information, namely Normal ('NO'), Alert1 ('A1'), Alert2 ('A2') normally used in museum security services, are considered. These commands are hidden over one plane of the four cover images and extracted at the receiver. The block size has been increased to embed these commands of size 16 bit. The time complexity of the proposed algorithm is analyzed in the given scenario and illustrated in Figure 14.1 It was observed that the proposed algorithm was capable of transmitting commands in less than 0.1 s, which is sufficient for museum security applications. At the control room, in each received frame, the region-II portion of the image shown in Figure 19 is segmented and is given to the Kalman filter for object detection and tracking. The tracking results for the six frames are given in Figure 20a-f. The green circle represents the Kalman filter estimation of the object, and the black circle represents the true position of the object.