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With the rapid development of the Internet and the robot binary math extraction service robot, the digital services are becoming important factors for robots to recognize things in the real world. Advances in computer technology and the risk of copyright infringement have increased in the way that anyone replicates and copies digital media easily.
Therefore, ways to prevent and suppress copyright infringement, digital watermarking technologies that insert copyrights information into the work, are being researched. Digital watermark, depending on the robot binary math extraction insert domain, can be categorized into the spatial domain and transform domain.
In this paper, we propose an extended DCT domain watermarking for robot vision. The suggested method is by distributing and duplicating watermark images that are including copyright information on the original images. By doing this, it can be strong on geometric image attacks such as partial cutting, resizing, rotating, and so forth.
For the success of watermark extraction, invertible process can be used to recover a disfigured image. Experimental results are provided to support these methods, through the process of recombination by complete image pieces. With the rapid development of robot services on the Internet, the digital services between robots and things using mobile terminals are becoming important issues.
Unlike the robotic technologies from the past, robots are holding greater performance robot binary math extraction our lives. Given that intellectual property of books, pictures, music, and so forth in digital format is reproduced in large quantities and distributed readily on the Internet, digital watermarking is drawing attention as key technology for copyright protection [ 1 ].
Digital watermarking algorithm is made up of two algorithms: This digital watermarking technology is classified into spatial domain watermarking and transforms domain watermarking depending on the domain into which the actual information is embedded.
In spatial domain watermarking, robot binary math extraction original signal is not converted but specific robot binary math extraction values are embedded as they are in the spatial domain [ 3 ]. Specific methods of this type include probabilistic labeling of a patchwork, as proposed by Bender et al. In transform domain watermarking, the original signal is converted and a watermark is embedded.
In addition, Cox et al. To overcome this shortcoming, in this paper an integrity verification system using a DCT-domain watermarking method is designed and implemented, which can be used to protect copyright of original contents and prevent forgery. Lastly, experimental results show that the process of image reverse conversion and extraction of the watermark image by recombining undamaged image pieces among overlapped watermark pieces can improve the extraction rate efficiently. The rest of the paper is organized as follows.
Several related works are compared and reviewed in the next section. Then, proposed methods for watermark insertion and extraction are proposed in Section 3. In Section 4 robot binary math extraction, the experimental results and analysis are given and conclusion provided in the final section, along with plans for further studies. DCT is a frequency linear transformation domain approach, which is characterized as more robust against attacks compared to the spatial time domain approach [ 9 ].
DCT is a method in robot binary math extraction conversion is done to frequency information in units of blocks by focusing widespread energy into several coefficients, as a result of which energy can be maximized in concentration [ 10 ].
DCT was introduced in a paper on a new type of orthogonal transformation called discrete cosine transform in by a team of three researchers at University of Texas, one of whom was Professor Rao. With DCT, contrary to how DFT calculates even complex numbers, only the real part is dealt with, so signal processing can be done effectively in the area of image processing, robot binary math extraction having to deal with the imaginary part [ 11 ]. In the inverse DCT stage, the frequency components are converted into an image in the spatial domain [ 12 ].
The method by Saeed [ 9 ], which is a DCT-domain watermarking method, supports the following methods: In Method 1A, a single watermark image is embedded and extracted. In Method 1B, two watermark images are embedded redundantly, which are extracted.
Then a new single watermark image is created by averaging the two watermark image information. Method 1C is basically the same as Method 1B except that three redundant watermark images are embedded. For Method 1B, it is strong against geometrical attacks such as size adjustment and rotation as the average of the redundant watermarks is used. For Method 1A, it is strong against noise attacks. For Method 1C, although the average is calculated as with Method 1B, it is relatively small because of the high number of redundancies.
The proposed scheme falls under robot binary math extraction watermarking. The embedded watermark should be extractable in its intact or slightest damaged condition even against malicious outside attack as well as general signal processing. As for the spatial domain-based watermarking, it has the advantage that the watermark can be inserted and extracted with a small amount of computations, leaving no damage to the quality of an image.
As methods for overcoming such a limitation of the spatial domain-based watermarking, transform domain-based techniques have been proposed, which transform the spatial domain signal of digital watermark into the frequency domain signal and insert the watermark into a visually less sensitive area among the frequency domains [ 3 ]. This study intends to restore the modified image to one close to the original through the process of image reverse conversion and then extract the watermark image by recombining undamaged image pieces among overlapped watermark pieces, whereby enhancing the robustness of watermark.
The proposed watermark insertion process is explained in Figures 3 and 4. This watermark image is separately stored as it is used during watermark extraction. The JPEG image compression standard will be used when the DCT is applied; to reduce the data size, a quantization process is went through, and to rearrange from 1-dimensional to 2-dimensional, zigzag scan is applied. As with the watermark image, the original image also goes through the process of quantization and zigzag scan.
A random number is generated using an embedding seed and the watermark image is embedded redundantly and dispersively. The insertion part is explained in detail in Figure 4. As shown in Figure 4watermark image pieces are embedded in the original image pieces redundantly and dispersively.
Up to 16 pieces can be redundantly embedded in the shape of a diamond in order to increase the watermark extraction rate, avoiding the frequency domain where there is a concentration of image information and each of the edges which is more likely to suffer damage from a partial cutting attack. The proposed watermark extraction process is described in Figures 5 and 6. The extraction part is explained in detail in Figure 8. In conclusion, watermark image pieces which were redundantly embedded are compared with matching pieces of the stored watermark image, and undamaged pieces are rearranged to increase the extraction success rate.
Also, for inverse transform of the watermark insertion image, JAI Java Advanced Imaging [ 14 ], a Java imaging processing library, was used. The implementation robot binary math extraction is shown in Table 2.
When the original image is loaded and the watermark text is inserted, the embedded watermark image is generated as shown in Figure 9. When watermark extraction is successfully extracted from the embedded watermark image, the extracted text can be checked, as shown in Figure The experimental units to be assessed in the test are shown in Table 3. A watermark is embedded in the original image, and partial cutting, size change, quality change, compression, rotation, and noise are applied to robot binary math extraction embedded watermark robot binary math extraction, after which watermark extraction is attempted.
If the watermark extraction fails, inverse transform is done according to the geometrical transform list, and watermark extraction is attempted again.
Experimental results show that proposed algorithm can be robust against many different types of geometric image attacks such as partial cutting, resizing, rotating, and so forth. Partial cutting was applied to parts that are different from one another in the embedded watermark image and then watermark extraction was attempted.
The results of partial cutting attacks are shown in Figure 11 for all the three watermarking schemes and it can be seen that, on average, extraction rate and PSNR are high for the proposed method, Method 1A, and Method 1B, in that order. Watermark extraction was attempted on two embedded watermark images which were reduced in size, and two robot binary math extraction watermark images which were increased in size. For the proposed method, the watermark was extracted after an image inverse transform.
An embedded watermark image was changed to four robot binary math extraction of different compression formats and watermark extraction was attempted. For the GIF format for the proposed method, image inverse transform was done before extracting the watermark. The embedded watermark image was rotated and watermark extraction was attempted on it.
Watermark extraction was attempted on four embedded watermark images which had different degrees of noise applied.
With the rapid development of the medium as image and video, digital watermarking is to use the digital embedding method to protect information [ 15 ], and copyright authentication based on watermarking becomes more and more urgent with wide spread of multimedia content over the Internet [ 15 ]. This study suggested an image integrity verification system utilizing DCT domain watermark, which is one of the transform domain based methods.
The suggested method is strong against partial cutting, filtering and noise by diffusing and overlapping the watermark image, which includes copyright information, onto the original image. By reassembling those undamaged and fully intact image pieces during extraction, watermark extraction rate can be increased even further.
After a watermark is extracted, its similarity to the watermark image being stored is compared. If the similarity is lower than some threshold, then the extracted image is deemed to be damaged and it is inverse transformed from the extractor to restore it; then, the watermark extraction process is done again. Watermark extraction rate could be increased over that in robot binary math extraction studies via redundant embedding of watermarks, reassembly of intact watermark image pieces, and image inverse transform process.
But as a stage-by-stage computational process is involved, there is the shortcoming that the proposed method has longer computational time than existing methods. Thus, some better features for reducing computational process will be considered to reduce the computational time in future work.
Home Journals About Us. Journal of Applied Mathematics. Subscribe to Table of Contents Alerts. Table of Contents Alerts. Abstract With the rapid development of the Internet and the mobile service robot, the digital services are becoming important factors for robots to recognize things in the real world.
Introduction With the rapid development of robot services on the Internet, the digital services between robot binary math extraction and things using mobile terminals are becoming important issues. Composition of a Digital Watermark A digital watermarking scheme is largely composed of the insertion part and the extraction part. Screen for original copy image a robot binary math extraction embedded watermark image b.
Kim, Multimedia Signal Processing: Han, DCT based watermarking using block robot binary math extraction [M. View at Scopus I. Robot binary math extraction at Google Scholar J.