A SIFT features based blind watermarking for DIBR 3D images

Nam, Seung-Hun; Kim, Wook-Hyoung; Mun, Seung-Min; Hou, Jong-Uk; Choi, Sunghee; Lee, Heung-Kyu

doi:10.1007/s11042-017-4678-x

Cited by 29 publications

(26 citation statements)

References 27 publications

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“…As reported in [14], DCT domain-based watermark embedding triggers block artifacts, and in the case of the DTCWT domain, the imperceptibility decreases due to the watermark insertion into the subbands generated through sampling of the decomposition process. Compared to the DTCWT domain, the NSCT domain without subsampling in the decomposition process has little degradation in image quality due to watermark insertion.…”

Section: Introductionmentioning

confidence: 80%

“…As we intended, our method shows higher watermark extraction performance against DIBR attacks, signal processing operations, and geometric distortions while maintaining higher imperceptibility than comparative methods [12]- [14]. In addition, the proposed method can extract embedded watermarks in a blind fashion while ensuring sufficient watermark capacity.…”

Section: Introductionmentioning

confidence: 82%

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NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

et al. 2020

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Depth-image-based rendering (DIBR), where arbitrary views are synthesized from a center image and depth image, has received much attention in the three-dimensional (3D) research field. With advances in depth-acquisition techniques and the proliferation of 3D glasses and 3D display devices, there is a growing demand for schemes to protect the copyrights of DIBR 3D images. Digital watermarking is a typical protection technology and designing a watermarking method for DIBR 3D images is a challenging task because the synchronization of watermarks can easily be broken in the process of generating synthetic images. To address this issue, we propose a non-subsampled contourlet transform (NSCT)-based blind watermarking for DIBR 3D images. To ensure the proposed method has properties of robustness against the DIBR process, we conduct an analysis of robustness for NSCT subbands against DIBR attacks. Based on the analysis results, we select subbands that are robust against DIBR attacks and embed watermark in the low coefficients of NSCT subbands using quantization-based embedding. While ensuring robustness, the proposed method also improves the imperceptibility of watermarks by adjusting their embedding strength according to computed perceptual masking values. Through experiments, we show that the proposed method is robust against both desynchronization attacks of the DIBR process and common attacks including signal processing operations and geometric distortions. The high imperceptibility of our method is also verified by several evaluation metrics in a subjective and objective manner. INDEX TERMS Depth-image-based rendering (DIBR), digital watermarking, DIBR 3D image watermarking, non-subsampled contourlet transform (NSCT), multi-bit watermarking.

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Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 82%

NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

et al. 2020

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“…DIBR is a rendering method to give a stereoscopic effect to images [ 40 , 41 ], but it cannot be protected by conventional watermarks and templates because it causes horizontal non-linear distortion. To cope with this, various DIBR watermarking techniques have been proposed [ 31 , 42 , 43 , 44 ], but they show weaknesses in geometric distortion since there have been few studies on the topic. The proposed learning-based and block-based template can easily solve the problem of geometric distortion for the DIBR watermark.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Architecture for Recovering Watermark Synchronization

Kim

Kang

Mun

et al. 2020

Sensors

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In this paper, we propose a convolutional neural network-based template architecture that compensates for the disadvantages of existing watermarking techniques that are vulnerable to geometric distortion. The proposed template consists of a template generation network, a template extraction network, and a template matching network. The template generation network generates a template in the form of noise and the template is inserted into certain pre-defined spatial locations of the image. The extraction network detects spatial locations where the template is inserted in the image. Finally, the template matching network estimates the parameters of the geometric distortion by comparing the shape of spatial locations where the template was inserted with the locations where the template was detected. It is possible to recover an image in its original geometrical form using the estimated parameters, and as a result, watermarks applied using existing watermarking techniques that are vulnerable to geometric distortion can be decoded normally.

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“…SIFT keypoint matching is a classical object-detection method used in natural image processing [18], however, it is difficult to apply these SIFT keypoints directly to detect the DBA from remote sensing images because remote sensing images generally present two major difficulties: large sizes and complex scenes. During the local descriptors extraction stage, the SIFT algorithm produces a large number of keypoints in nonbuilding areas.…”

Section: Screening Of Keypoints In Building Areas Based On the Structmentioning

confidence: 99%

Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Lei

Yang

et al. 2019

Photonic Sens

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Automatic detection of a designated building area (DBA) is a research hotspot in the field of target detection using remote sensing images. Target detection is urgently needed for tasks such as illegal building monitoring, dynamic land use monitoring, antiterrorism efforts, and military reconnaissance. The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes. To address the problems of the current detection methods, this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images. Our method was conducted in two main stages. (1) During keypoint generation, we proposed a screening method based on structural pattern descriptors. The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram (MLPH) feature; then, we used one-class support vector machine (OC-SVM) merely to screen those building attribute keypoints. (2) To match the screened keypoints, we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints. We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection. We tested the proposed method on building area sets of different types and at different time phases. The experimental results show that the proposed method is both highly accurate and computationally efficient.

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A SIFT features based blind watermarking for DIBR 3D images

Cited by 29 publications

References 27 publications

NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

NSCT-Based Robust and Perceptual Watermarking for DIBR 3D Images

Convolutional Neural Network Architecture for Recovering Watermark Synchronization

Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

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