3D watermarking scheme in stereo vision system

Hwang, Dong‐Choon; Bae, Kwang‐Hak; Ko, Jung−Hwan; Kim, Eun-Soo

doi:10.1117/12.619602

Cited by 2 publications

(12 citation statements)

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“…The decoding data requirement indicates the amount of data needed for extracting information from the received image. In the data payload category, it can be observed that relative to the work performed in [24], [32], [33], [34], [35] and [36], the proposed system does not outperform the reported work. However, when compared to the above reported works, excluding [24], the proposed system outperforms these systems in the area of decoding data requirements.…”

Section: Watermark Characteristicsmentioning

confidence: 98%

“…The work in [35] and [36] proposes a novel watermarking scheme while exploring the benefits of different frequency domain transformations. In addition, they evaluate different disparity map estimation techniques.…”

Section: Disparity Map Image Watermarkingmentioning

confidence: 99%

“…In addition, they evaluate different disparity map estimation techniques. This section will begin with a discussion of the embedding process used in [35] and [36] and continues by specifying the transforms and disparity map cost functions that were used. The embedding process begins through the transformation of the Right Image into a frequency domain, after which the values are quantized.…”

Section: Disparity Map Image Watermarkingmentioning

confidence: 99%

“…This watermarking scheme would make it impossible for the detector to retrieve any information from the left image and relies solely on the right image to be transmitted for the detection process. This problem is also faced in the work presented in [35], [37], and [36]. However, the work in [26] stored information in the disparity map, and this information can only be detected once the right image is rendered, while in [33] and [34] it is stored in the disparity map.…”

Section: Limitations Of Existing Workmentioning

confidence: 99%

“…The initial inspiration for the pre-processing of the disparity map came from the work in [35] and [36]. The motivation behind the adoption of this mechanism is the lack of smoothness within the disparity map.…”

Section: Novelty Of the Proposed Systemmentioning

confidence: 99%

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Digital watermarking of 3D disparity map based images for transaction tracking

Jaber¹

2021

Preprint

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In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.

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