scholarly journals Data Hiding in Symmetric Circular String Art

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1227
Author(s):  
Yu-Song Yan ◽  
Hui-Li Cai ◽  
Bin Yan
Keyword(s):  
Data Hiding ◽  
Copyright Protection ◽  
Vision System ◽  
Human Vision ◽  
Secret Data ◽  
Filter Model ◽  
Minimal Distortion ◽  
Computational Resources ◽  
The Impact ◽  
Intellectual Properties

Creative digital artwork is usually the outcome of a long period of intellectual creation and labor of an artist. Similarly, computer-created digital artwork is an outcome of a large amount of machine time and computational resources. However, such intellectual properties can be easily copied by illegal users. Copyright protection of digital art is increasingly more important than before. Recently, using a computational approach to generate string art tends to be popular and attractive. To protect the illegal usage of the digital form of string art, we propose a data hiding algorithm specifically designed for string art. A digital string art image consists of a sequence of string lines, each specified by two nails fixed at the two ends of that line. The encrypted secret data (the watermark) is embedded into the list of line segments by odd–even modulation, where a bit ‘1’ is embedded by forcing the next node to be an odd node, and a bit ‘0’ is embedded by forcing the next node to be an even node. To minimize the impact of data embedding on the quality of the original string art image, a local optimization algorithm is developed to select the nodes that produce minimal distortion. To quantify the embedding distortion, we introduce a smoothing filter model for the human vision system (HVS) specifically tailored to string art image. Experimental results show that using the proposed algorithm, the distortion between the original string art image and the watermarked string art image is unnoticeable. The modified string art image is statistically indistinguishable from the original string art, and hence is secure under steganalysis. To our best knowledge, this is the first work towards data hiding and copyright protection of digital string art.

scholarly journals Data-Hiding Scheme Using Multidirectional Pixel-Value Differencing on Colour Images

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Pyung-Han Kim ◽  
Eun-Jun Yoon ◽  
Kwan-Woo Ryu ◽  
Ki-Hyun Jung
Keyword(s):  
Data Hiding ◽  
Vision System ◽  
Experimental Results ◽  
Human Vision ◽  
Embedding Capacity ◽  
Secret Data ◽  
Colour Image ◽  
Pixel Value ◽  
The Difference ◽  
Pixel Value Differencing

Data hiding is a technique that hides the existence of secret data from malicious attackers. In this paper, we propose a new data-hiding scheme using multidirectional pixel-value differencing, which can embed secret data in two directions or three directions on colour images. The cover colour image is divided into nonoverlapping blocks, and the pixels of each block are decomposed into R, G, and B channels. The pixels of each block perform regrouping, and then the minimum pixel value within each block is selected. The secret data can be embedded into two directions or three directions based on the minimum pixel value by using the difference value for the block. The pixel pairs with the embedded secret data are put separately into two stego images for secret data extraction on receiver sides. In the extraction process, the secret data can be extracted using the difference value of the two stego images. Experimental results show that the proposed scheme has the highest embedding capacity when the secret data are embedded into three directions. Experimental results also show that the proposed scheme has a high embedding capacity while maintaining the degree of distortion that cannot be perceived by human vision system for two directions.

scholarly journals Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1974 ◽  
Author(s):  
Yu-Hsiu Lin ◽  
Chih-Hsien Hsia ◽  
Bo-Yan Chen ◽  
Yung-Yao Chen
Keyword(s):  
Image Compression ◽  
Data Hiding ◽  
Smart Cities ◽  
Vision System ◽  
Transmission Efficiency ◽  
Human Vision ◽  
Security Issue ◽  
Secret Data ◽  
Iot Security ◽  
Compressed Images

This study investigates combining the property of human vision system and a 2-phase data hiding strategy to improve the visual quality of data-embedded compressed images. The visual Internet of Things (IoT) is indispensable in smart cities, where different sources of visual data are collected for more efficient management. With the transmission through the public network, security issue becomes critical. Moreover, for the sake of increasing transmission efficiency, image compression is widely used. In order to respond to both needs, we present a novel data hiding scheme for image compression with Absolute Moment Block Truncation Coding (AMBTC). Embedding secure data in digital images has broad security uses, e.g., image authentication, prevention of forgery attacks, and intellectual property protection. The proposed method embeds data into an AMBTC block by two phases. In the intra-block embedding phase, a hidden function is proposed, where the five AMBTC parameters are extracted and manipulated to embed the secret data. In the inter-block embedding phase, the relevance of high mean and low mean values between adjacent blocks are exploited to embed additional secret data in a reversible way. Between these two embedding phases, a halftoning scheme called direct binary search is integrated to efficiently improve the image quality without changing the fixed parameters. The modulo operator is used for data extraction. The advantages of this study contain two aspects. First, data hiding is an essential area of research for increasing the IoT security. Second, hiding in compressed images instead of original images can improve the network transmission efficiency. The experimental results demonstrate the effectiveness and superiority of the proposed method.

The Efficiency of Data Hiding by Manipulating the DPCM Macroblocks in Compressed Video

2011 ◽  
Vol 230-232 ◽  
pp. 69-74
Author(s):  
Tarik Idbeaa ◽  
Kasmiran Jumari ◽  
Salina Abd. Samad ◽  
Ali Abdulgader ◽  
Nidal Eshah
Keyword(s):  
Video Compression ◽  
Data Hiding ◽  
Significant Loss ◽  
Low Noise ◽  
Video Data ◽  
Human Vision ◽  
Compressed Video ◽  
Secret Data ◽  
Still Images ◽  
High Security

Steganography is the idea of embedding a secret data in different media and has become an important regulation of methods of data integration. Although the still images are generally applied in the past, is very popular in recent years for the video. The techniques of video data hiding in recent year’s emphasis on the features generated by the video compression standard, a safer method for steganography uses MPEG-4/H.264 Bit Plane Complexity Segmentation (BPCS) algorithm is proposed in this approach. The reason for choosing such a video coverage is the enormous amount of data that can be hidden in each frame of MPEG-4 video. In other words, MPEG-4 has three types of images: I-frame, B, and P frames. Unlike other techniques used to hide data, such as the LSB algorithm, PBCS can achieve better results in both mathematics expression and human vision. In this paper, data is embedded in the videos of the I-frame until the BPCS can reach high levels of integration with low distortion based on the theory that regions of low noise-levels as in a picture can be replaced by noise without a significant loss of image quality. This approach invents data hidden in high-security environments. Experimental results show the success of hidden data in the selected and extracted data from the sequence of frames and also indicate the effectiveness of the implementation plan of steganography compressed video with high security features.

scholarly journals Viewing Direction Based LSB Data Hiding in 360° Videos

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1527
Author(s):  
Dang Ninh Tran ◽  
Hans-Jürgen Zepernick ◽  
Thi My Chinh Chu
Keyword(s):  
Data Hiding ◽  
Gaussian Mixture Models ◽  
Visual Quality ◽  
Structural Similarity ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Weight Functions ◽  
Least Significant Bit ◽  
Secret Data ◽  
The Impact

In this paper, we propose a viewing direction based least significant bit (LSB) data hiding method for 360° videos. The distributions of viewing direction frequency for latitude and longitude are used to control the amount of secret data to be hidden at the latitude, longitude, or both latitude and longitude of 360° videos. Normalized Gaussian mixture models mimicking the viewing behavior of humans are formulated to define data hiding weight functions for latitude, longitude, and both latitude and longitude. On this basis, analytical expressions for the capacity offered by the proposed method to hide secret data in 360° cover videos are derived. Numerical results for the capacity using different numbers of bit planes and popular 360° video resolutions for data hiding are provided. The fidelity of the proposed method is assessed in terms of the peak signal-to-noise ratio (PSNR), weighted-to-spherically uniform PSNR (WS-PSNR), and non-content-based perceptual PSNR (NCP-PSNR). The experimental results illustrate that NCP-PSNR returns the highest fidelity because it gives lower weights to the impact of LSB data hiding on fidelity outside the front regions near the equator. The visual quality of the proposed method as perceived by humans is assessed using the structural similarity (SSIM) index and the non-content-based perceptual SSIM (NCP-SSIM) index. The experimental results show that both SSIM-based metrics are able to account for the spatial perceptual information of different scenes while the PSNR-based fidelity metrics cannot exploit this information. Furthermore, NCP-SSIM reflects much better the impact of the proposed method on visual quality with respect to viewing directions compared to SSIM.

scholarly journals Development Of In-Vehicle Collision Warning System For Intersections

2021 ◽  
Author(s):  
Essam M.S.A.E.A. Dabbour
Keyword(s):  
Statistical Models ◽  
Human Error ◽  
Vision System ◽  
Warning System ◽  
Network Models ◽  
Human Vision ◽  
Human Vision System ◽  
Neural Network Models ◽  
Collision Warning ◽  
The Impact

Most of the current collision warning systems are mainly designed to detect imminent rear-end, lane-changing or lane departure collisions. None of them was designed to detect imminent intersection collisions, which were found to cause more fatalities and injuries than other types of collisions. One of the most important factors that lead to intersection collisions is driver’s human error and misjudgement. A main source for human errors is the insensitivity of human vision system to detect the speed and acceleration of approaching vehicles; and therefore, any algorithm for an intersection collision warning system should give consideration to the speed and acceleration of all approaching vehicles to mitigate the inadequacy in the human vision system. Moreover, when designing any collision warning system, false warnings should be minimized to avoid nuisance for drivers that might lead to the loss of the system’s reliability by potential users. This research proposed an intersection collision warning system that utilizes commercially-available detection sensors to detect approaching vehicles and measure their speeds and acceleration rates in order to estimate the time-to-collision and compare it to the time required for the turning vehicle to clear the paths of the approaching vehicles. By comparing these times, the system triggers a warning message if an imminent collision is detected. Minimum specifications for key hardware components are established for the proposed system which does not depend on specific technology. To estimate the time require to clear the paths of the approaching vehicles, statistical models were developed to estimate the perception-reaction time for the driver of the turning vehicle and the rate of acceleration selected when departing the intersection. The statistical models include regression models that were calibrated from data collected through driving simulation and more-sophisticated artificial neural network models that are based on actual data collected from a specific driver on a specific vehicle. The proposed system was validated by computer simulation to verify the accuracy of the developed algorithms and to measure the impact of different components on the functionality and reliability of the system. Final conclusions are provided along with recommendations for further research.

scholarly journals Development Of In-Vehicle Collision Warning System For Intersections

2021 ◽  
Author(s):  
Essam M.S.A.E.A. Dabbour
Keyword(s):  
Statistical Models ◽  
Human Error ◽  
Vision System ◽  
Warning System ◽  
Network Models ◽  
Human Vision ◽  
Human Vision System ◽  
Neural Network Models ◽  
Collision Warning ◽  
The Impact

Most of the current collision warning systems are mainly designed to detect imminent rear-end, lane-changing or lane departure collisions. None of them was designed to detect imminent intersection collisions, which were found to cause more fatalities and injuries than other types of collisions. One of the most important factors that lead to intersection collisions is driver’s human error and misjudgement. A main source for human errors is the insensitivity of human vision system to detect the speed and acceleration of approaching vehicles; and therefore, any algorithm for an intersection collision warning system should give consideration to the speed and acceleration of all approaching vehicles to mitigate the inadequacy in the human vision system. Moreover, when designing any collision warning system, false warnings should be minimized to avoid nuisance for drivers that might lead to the loss of the system’s reliability by potential users. This research proposed an intersection collision warning system that utilizes commercially-available detection sensors to detect approaching vehicles and measure their speeds and acceleration rates in order to estimate the time-to-collision and compare it to the time required for the turning vehicle to clear the paths of the approaching vehicles. By comparing these times, the system triggers a warning message if an imminent collision is detected. Minimum specifications for key hardware components are established for the proposed system which does not depend on specific technology. To estimate the time require to clear the paths of the approaching vehicles, statistical models were developed to estimate the perception-reaction time for the driver of the turning vehicle and the rate of acceleration selected when departing the intersection. The statistical models include regression models that were calibrated from data collected through driving simulation and more-sophisticated artificial neural network models that are based on actual data collected from a specific driver on a specific vehicle. The proposed system was validated by computer simulation to verify the accuracy of the developed algorithms and to measure the impact of different components on the functionality and reliability of the system. Final conclusions are provided along with recommendations for further research.

A robust and high capacity data hiding method for JPEG compressed images with SVD-based block selection and advanced error correcting techniques

2021 ◽  
pp. 1-11
Author(s):  
Kusan Biswas
Keyword(s):  
Data Hiding ◽  
High Capacity ◽  
Error Correcting Codes ◽  
Human Vision ◽  
Data Embedding ◽  
Compressed Images ◽  
Matrix Encoding ◽  
Dct Coefficients ◽  
Capacity Data ◽  
Value Decomposition

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.

scholarly journals Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 917
Author(s):  
Limengnan Zhou ◽  
Hongyu Han ◽  
Hanzhou Wu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Hot Spot ◽  
Data Embedding ◽  
Histogram Shifting ◽  
Secret Data ◽  
Content Selection ◽  
Wide Range ◽  
Map Generation ◽  
Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

scholarly journals Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 921
Author(s):  
Rui Wang ◽  
Guohua Wu ◽  
Qiuhua Wang ◽  
Lifeng Yuan ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Personal Information ◽  
Rapid Development ◽  
Prediction Method ◽  
Prediction Errors ◽  
Edge Detector ◽  
Secret Data ◽  
Encrypted Images ◽  
Cloud Servers

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

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