DWT-Based Data Hiding Technique for Videos Ownership Protection DOI: http://dx.doi.org/10.5772/intechopen.84963

this can be attributed to the blurring effects in these frames as a result of the fast panning of the camera. This, in turn, would result in lower energy in the DWT coefficients, and it is possible to overcome this phenomenon by our enhanced detection process as we will see later.

The correlations, moreover, were evaluated when the proposed detection process was applied. The watermarks were embedded randomly in multiple frames; then they were extracted and processed according to the method in Section 3. A number of frames being used for embedding process are 20%, 50%, and ultimately 100% of the 100 test frames of the proposed standard videos. Figure 19 shows the performance under these circumstances. It can be shown that the detection process was enhanced dramatically when comparing with Figure 18. The system can perform well even with only 20% of the frames being watermarked. To evaluate our algorithm under different compression ratios, the standard test videos were watermarked and compressed using HEVC with different QPs: 15, 20, and 25. The value of 20 is a typical value for compression. Figure 20 shows the performance of our system under these compression values. As QP becomes larger than 25, the video qualities go through noticeable degradation in terms of resolutions; in fact, the system performs well for QP values of 20 or less, and as QP values reach 25, the detection process starts to lose its efficiency for some videos. This is due to the aggressive quantization process of the discrete cosine transform (DCT) coefficients in the HEVC process.

Our selective denoising filter which was introduced in Section 4 was tested for the watermarked videos. The standard videos were watermarked according to the proposed embedding process; then they were subjected to salt-and-pepper noise

Figure 19.

The results for each attack for the different standard videos are shown

Performance of the watermarking process under common aggressive attacks.

PSNRs of the proposed watermarking method when HEVC process is applied.

Normalized correlations of the proposed watermarking method when HEVC process is applied.

To ensure the robustness of the algorithm, it was tested with the application of HEVC process. HEVC process with a quantization parameter (QP) value of 20 was applied to 100 frames of the test videos. Different compression ratios will result depending on each input video when this quantization factor is used. First, the watermarking process was applied to the test video frames and without applying the enhancement process. The PSNRs are shown in Figure 17, and the NCs at the watermarked frames are shown in Figure 18. A significant observation here is that lower values will result in the frames between 80 and 90 for the Football video, and

in Figure 16.

Wavelet Transform and Complexity

Figure 16.

Figure 17.

Figure 18.

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Correlations vs. number of frames being watermarked when our detection method is used.

Figure 20. Watermarking process performance under different quantization parameters.

with 1% density. At the receiving side, the videos were denoised using the selective filter; then the watermarks were extracted. Figure 21 shows the normalized correlations and the PSNRs under these conditions. It is clear that the selective filtering scheme enhanced the visual appearance by eliminating the noise without significant effects on the efficiency of the watermarking process. Furthermore, Figure 22 shows the results when 2D median filter is used. It can be seen that our selective denoising filter outperformed the 2D median filter in terms of the correlation

values. The PSNR values are almost comparable, even though when assessing the quality of the images, the metric parameters are not the only factor that should be taken into consideration. The perceptual quality is another factor which was better

Since the security is an important aspect for our algorithm and any data hiding technique, false alarm attacks were studied for our test videos. That happens when no hiding process was done or a false watermark was hidden and still the system indicates the existence of our watermark. To do that, we generate 500 different random watermarks and hide them in the test videos according to our proposed algorithm, and the right watermark was one of them and it was set to be the 350th one. Figure 23 shows the results. It can be seen that the response of our system was

This work proposes a DWT-based watermarking process using randomly generated orthonormal filter banks. An enhanced detection process was proposed to add to the robustness of the system. Moreover, a selective filtering process was developed to eliminate the noise. A good deal of the security of the system was achieved by the randomness in the filter banks, the pseudorandom sequence that was used to encode the watermark, and the regions of hidings. It was shown that the proposed technique performs well with and without HEVC. The compression ratio that was used is typical. Further investigation of the efficiency of the watermarking process under other aggressive attacks will be discussed and researched in future work. Moreover, an integration process of the data hiding process inside videos and the

The authors whose names are listed above certify that there are no conflicts of interests of any sort or nature between any of them and any institution or organization (public or private) that have special interest in the research that is the topic

low to the false watermarks, and only the right watermark resulted in high

response. This is an indication of good reliability of our system.

with our own filter due to the selectivity process.

DOI: http://dx.doi.org/10.5772/intechopen.84963

DWT-Based Data Hiding Technique for Videos Ownership Protection

6. Conclusions and future work

HEVC process will be studied and investigated.

Conflict of interest

of this work.

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### Figure 21.

The performance of the watermarking process with the use of the proposed selective filter where the blue line is the correlation and the green line is the PSNR.

### Figure 22.

The performance of the watermarking process with the use of a 2D median filter where the blue line is the correlation and the green line is the PSNR.

### Figure 23.

The watermarking process response to false alarm test, the right watermark is the 350th with different videos: (a) Akiyo, (b) Foreman, (c) Football, (d) BasketballDrill, and (e) BasketballDrive.

## DWT-Based Data Hiding Technique for Videos Ownership Protection DOI: http://dx.doi.org/10.5772/intechopen.84963

values. The PSNR values are almost comparable, even though when assessing the quality of the images, the metric parameters are not the only factor that should be taken into consideration. The perceptual quality is another factor which was better with our own filter due to the selectivity process.

Since the security is an important aspect for our algorithm and any data hiding technique, false alarm attacks were studied for our test videos. That happens when no hiding process was done or a false watermark was hidden and still the system indicates the existence of our watermark. To do that, we generate 500 different random watermarks and hide them in the test videos according to our proposed algorithm, and the right watermark was one of them and it was set to be the 350th one. Figure 23 shows the results. It can be seen that the response of our system was low to the false watermarks, and only the right watermark resulted in high response. This is an indication of good reliability of our system.
