Wavelet-based data hiding of DEM in the context of real-time 3D visualization

Hayat, Khizar; Puech, William; Gesquière, Gilles; Chaumont, Marc

doi:10.1117/12.703040

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Aside from steganography, good examples of information hiding in images have been explored in [20], [21], [22]. Notably, in these studies, the authors have encoded extra information about the visible image within the transmitted image, for example color information in a gray-scale version of the same image.…”

Section: ç 1 Introductionmentioning

confidence: 99%

Hiding Images within Images

Baluja

2020

IEEE Trans. Pattern Anal. Mach. Intell.

170

107

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We present a system to hide a full color image inside another of the same size with minimal quality loss to either image. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we examine how the result is achieved and apply numerous transformations to analyze if image quality in the host and hidden image can be maintained. These transformation range from simple image manipulations to sophisticated machine learning-based adversaries. Two extensions to the basic system are presented that mitigate the possibility of discovering the content of the hidden image. With these extensions, not only can the hidden information be kept secure, but the system can be used to hide even more than a single image. Applications for this technology include image authentication, digital watermarks, finding exact regions of image manipulation, and storing meta-information about image rendering and content.

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Section: ç 1 Introductionmentioning

confidence: 99%

Hiding Images within Images

Baluja

2020

IEEE Trans. Pattern Anal. Mach. Intell.

170

107

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“…With the increasing use of DEMs in many fields, such as 3D terrain visualization, hydrological, ecological, and geomorphological analysis [1][2][3][4], it is now necessary to obtain high resolution DEMs for large areas. A high-resolution DEM contains more information and can better reflect the actual surface, which plays a crucial role in the correct derivation of terrain factors such as slope, aspect, and the topographic wetness index [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

et al. 2021

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High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

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“…Digital elevation model (DEM) data is one of the most common data in geographic analysis, and its application scenarios are fairly extensive [1][2][3][4]. The security of the DEM data has always been the research hotspot [5][6][7], in which digital watermarking is the main approach for copyright protection and integrity authentication for the DEM data [8,9]. Digital watermarking refers to the practice of imperceptibly modifying the data to embed watermark information, whether in the space domain or the transform domain [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Zero Watermarking for the TIN DEM Data Based on the Edge Length

Zhou

Zhu

Ren

2021

IJGI

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How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) DEM data. It takes full advantage of the characteristics of the edge length in the TIN DEM data. First, the radio of the edge lengths is quantified to the watermark index, and then the comparison of the edge lengths is quantified to the watermark bit. Finally, the watermark is constructed by combing the watermark bits according to the watermark indices with the help of the majority voting mechanism. In the method, the TIN DEM data are only used to construct the watermark, not to be embedded by the watermark. Therefore, the data quality is preserved to the greatest extent. Experiments verify the theoretical achievements of this method and demonstrate the method is lossless to the TIN DEM data. Simulation results also show that the method has good robustness on translation, rotation, scaling, and cropping attacks.

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Wavelet-based data hiding of DEM in the context of real-time 3D visualization

Cited by 5 publications

References 26 publications

Hiding Images within Images

Hiding Images within Images

An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

Zero Watermarking for the TIN DEM Data Based on the Edge Length

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