A Novel Reversible Data Hiding Algorithm Based on Probabilistic XOR Secret Sharing in Wavelet Transform Domain

Fragile watermarking methods have been used as active image authentication methods. These methods must have capabilities like high capacity, high visual quality, high image authentication ability and image recovery capability. Many of the fragile watermarking methods suggested in the literature do not provide all of these capabilities. In this article, a new probabilistic image authentication method with high capacity, high visual quality, high image authenticity ability and image recovery capability is proposed. Chaotic pseudo random number generators, a new watermark generation network which similar to neural network, modulo based watermark embedding functions, modulo based watermark extraction functions, tamper detection algorithm and perceptual hash based image recovery algorithm are utilized for creating a new probabilistic image authentication method in this article. The proposed method is a block based authentication method and 3 × 3 size of overlapping neighborhood blocks are used in this method. The proposed watermark generation network uses symmetric pixels as inputs and weights to generate predicted pixel. In this step, modulo operator and random numbers are used for calculating embeddable value. Owing to chaotic random number generator, the proposed method gains probabilistic and nonlinear structure. To generate random number, logistic map is used as random number generator in the proposed image watermarking method. Then, a modulo based watermark embedding function is used for embedding embeddable value (generated watermark). In the watermark extraction step, modulo operator is used. If the predicted value differs from the extracted value, tamper detection is performed. Otherwise, image authentication is success. A new image recovery algorithm is proposed in this method. This algorithm provides image recovery for tampered areas with high visual quality. The proposed image watermarking method can authenticate grayscale and color images. The experimental results demonstrated that, the proposed method has high payload capacity, high visual quality and high image authentication ability.

Reversible watermarking inserts watermarks into digital media in such a way that visual transparency is preserved, which enables the restoration of the original media from the watermarked one without any loss of media quality. It has various applications where high capacity and high visual quality are major requirements for reversible watermarking. This work presents a new reversible watermarking algorithm that embeds message bits by modifying the differential histogram of adjacent pixels. To satisfy both high embedding capacity and visual quality, the proposed technique exploits the fact that the adjacent pixels are highly correlated. Also, we prevent overflow and underflow problems by designing a predicted error compensation scheme. Through experiments using multiple kinds of test images, we prove that the presented algorithm provides 100% reversibility, higher capacity, and higher visual quality than any previous method, while maintaining low induced distortion.

The effect of plane of nutrition during rearing on growth, production, reproduction and health of dairy cattle. III. Milk production during the first three lactations

A high capacity reversible data hiding scheme based on right-left shift

Fragile watermarking methods have been widely used for image authentication. A novel image-authentication method based on fuzzy coding is presented in this work. This method is a block-based method and it consists of watermark generation, watermark embedding, watermark extraction, tamper detection, and image recovery phases. First, the cover image is divided into nonoverlapping blocks. In the watermark-generation step, fuzzy sets and fuzzy coding are used. To generate fuzzy sets, three threshold points are selected from a histogram of the cover image and fuzzy sets are created by using these threshold points. Memberships of pixels are calculated to generate features. Fuzzy coding is used to generate the watermark. The watermark-embedding function uses ±1 data hiding function for each block. Therefore, high visual quality is achieved. The generated watermark is compared with the extracted watermark in the tamper-detection phase. Finally, a new image-recovery algorithm is proposed for recovering tampered areas. To evaluate the proposed method, visual quality, tamper detection ability, image recovery capability, and computational complexity were used. The experimental results clearly indicated that the proposed method was a success.

In this paper, we present a new image steganography technique based on Integer Wavelet Transform (IWT) and Munkres' assignment algorithm which embeds secret image in frequency domain of cover image with high matching quality. IWT is used to transform both cover and secret images from spatial domain to frequency domain, and assignment algorithm is used for best matching between blocks for embedding. We embed the secret image in different coefficients of cover image bands such as horizontal detail, vertical detail and diagonal detail and observe the effect of embedding on the performance of stego image in terms of Peak Signal to Noise Ratio (PSNR). Experimental results depict that stego image and extracted secret image could have high visual quality and they are perceptually similar to their original versions. In addition, this method shows high robustness against six different attacks. Embedding in diagonal detail coefficients gives better PSNR than other coefficients.

In today's world of fast communication, to insure, the security and integrity of information is a big challenge. Data hiding also known as Steganography is one of the fields that deal in methods related information security and hide secret information and message other information. This paper elucidates the effect of data hiding in different number of least significant bits in the primary colors of RGB color image. These individual color channels are analyzed at different hiding capacity level and its has been observed that high quality Stego images with PNSR 30dB and above can been obtained by hiding secret information in 5 least significant bits of red, green and blue channels, specially the green and blue channels give a very high visual quality. The individual channels can hide 20% data, i.e. one fifth of the overall size of cover image, with undetectable changes in cover image.

A significant data protection technique for a number of problems, the most prominent being identity authentication and copyright protection, is digital image watermarking. The rapid digital transformation of the world has given rise to a variety of vision modification techniques, which have significant consequences for picture data security. As a result, maintaining the validity and integrity of digital images is crucial, which is why researchers are focusing on creating effective watermarking techniques. This study proposes an optimized robust watermarking technique based on lifting wavelet transform (LWT) with singular value decomposition (SVD) using the particle swarm optimization (PSO) algorithm for achieving multiple scaling factors (MSF). To increase security and durability, cover images are exposed to numerous attacks. The evaluation criteria, which encompass normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR), were used to compare our outcomes with those of leading watermarking methods. The comparison reveals that our proposed strategy surpasses existing methods in terms of both robustness and imperceptibility. The results suggest that this technique is suitable for tamper detection in various domains, including cryptography, medical imaging and multimedia transmission.

Steganography allows a user to hide the large amount of secret data within a image and audio files. It has become an interesting research area to enhance the security of network communications, copyright protection etc. The simplest methodology to hiding the data within an image is called LSB substitution method. LSB substitution method take the binary representation of hidden data and overwrite the LSB of each byte with in a cover image. In the proposed system the text data can be embedded into the LSB of cover image using Pixel Indicators in a Zigzag manner. The aim is to provide a better security with high embedding capacity and a better stego image quality than the existing systems. There are two different indicators are used, based on those indicators used in a Zigzag manner the hidden data is embedded into the LSB of the respective cover image. This type of embedding technique making it difficult for any attacker to extract the hidden data from the stego-image. Peak Signal-to-Noise Ratio is used to measure the quality of the stego image. The proposed method provides better PSNR value than the existing systems.

In steganography, the cover medium is widely treated as a mere container for the embedded information, even though it affects the stego-image quality, security, and robustness. In addition, there is no consensus on the characteristics of a suitable cover image. In this work, we introduce and practically prove the most suitable cover image (MSCI) framework to automatically select a cover image for a given secret image. This paper proposes choosing the most suitable cover from a set of images based on two steps. First, a set of cover images is filtered based on relative entropy and a histogram in order to identify the most suitable candidates. Second, the local block pixel intensity features of the candidates are analyzed to select the most suitable cover image. Furthermore, cover image local blocks were optimized, using rotation and flipping, during the embedding process to further improve stego-image representation. The proposed framework demonstrated high visual image quality when compared with existing solutions. Steganalysis tests indicated that the proposed solution for cover selection provided an increased resistance to modern steganalyzers with up to 30% lowered detection rate, which improved security.

In this paper, an effective method named Exponential Fractional-Cat Swarm Optimization (Exponential Fractional-CSO) along with multi-objective cost function is proposed. The proposed method is designed by integrating the CSO with the fractional concept based on the Exponential parameters. Initially, an input video is selected from the database from which frames are generated. Key frames are chosen among the frames using the contourlet transform and Structural Similarity Index Measure (SSIM). Regions are formed on the selected key frames through the help of grid lines. Once the regions are formed, optimal regions are ascertained with the help of the proposed optimization algorithm along with multi-objective cost functions to hide the secret data. During the embedding process, the secret data is hidden in the optimal region using the lifting wavelet transform (LWT). The embedded video is then transmitted through the network to reach its intended receiver. The experimental results reveal that the proposed Exponential Fractional-CSO obtained a maximal correlation of 0.9931 by considering the frames, maximal Peak Signal-to-Noise Ratio (PSNR) of 89.70 dB and MSE of 0.00006 respectively. Hence, the proposed method shows greater effectiveness of hiding the secret data in the video sequence along with data security.

Steganography plays a vital role in the field of covert communication. This paper proposes a novel embedding scheme which embeds secret data adaptively. The proposed scheme employs integer to integer Lifting Wavelet Transforms (LWT) and pixel strength to specify suitable locations for data embedding. The integer LWT eliminates distortions, which is produced because of rounding process in the convolutional wavelet transforms. The performance of the proposed scheme is addressed using image quality metrics based on Peak Signal To Noise Ratio (PSNR) versus embedding ratio. Moreover, Bit Error Rate (BER) is utilized to assess robustness of the scheme. The experimental results show that the proposed is superior over several embedding techniques such as embedding based convolutional wavelet transforms and Lest Significant Bits (LSBs). The range of PSNR is gained between 51.52 and 22.15dB for range of embedding rate between 5% and 35%. Furthermore, the technique results error free for retrieving of secret data. In addition to the above features, the proposed technique increases security level of embedded data due to adaptive embedding process that increases randomly of embedding locations. The proposed scheme can be used in several important applications such as bank tag for authentication, military communications. Also, it can be implemented easily as a hardware chip and combine with covert imaging systems.

Steganography is the art and science of hiding secret data to provide a safe communication between two parties and it is a prominent branch in the information hiding research area. This paper presents a new steganographic method based on predictive coding and embeds secret message in quantized error values via Quantization Index Modulation (QIM). The proposed method is superior to previous methods in that it can make a satisfying balance among the most concerned criteria in steganography which are imperceptibility, hiding capacity, compression ratio and robustness against attacks. The performance of the proposed method is evaluated by several experiments on gray-level images with different textural properties. The new method is also compared with two renowned steganographic methods namely Jsteg and Steganography Based on Predictive Coding (SBPC). The results obtained from the experiments show that the proposed method has high visual quality and less histogram distortion while it has satisfactory compression ratio and embedding size.

In this paper, an effective real-time compression method is proposed to compress the tremendous amount of data generated in continuous spatio-temporal monitoring of power transmission cables with the distributed fiber-optic vibration sensor(DFOVS). The proposed method mainly consists of anomaly detection by statistical methods and Lifting Wavelet Transform(LWT) with an adaptive OTSU threshold. The experiment results for real field data indicate that the average compression ratio of this method can reach 138.9. Therefore the proposed method can effectively solve the challenging problems of real-time storage and transmission for big data acquired from a high density spatio-temporal acquisition grid. Besides, LWT has a higher calculation efficiency than the conventional Discrete Wavelet Transform (DWT) in real applications. And the data storage time can be obviously saved by using the proposed method on the big data processing platform of HDFS and HBase. The overall compression time is generally less than the massive data generation time in long distance or large range monitoring with DFOVS, thus it can be used for real-time compression of similar applications with moderate sampling rate.

In order to realize copyright protection and piracy tracking for ever-increasing videos, video information hiding has gained more and more attention. For the latest video coding standard — High Efficiency Video Coding (HEVC), most of the information hiding algorithms are based on the same characteristics as those in MPEG4 and H.264, such as DCT coefficients, intra prediction modules and motion vectors. However, few algorithms are reported to hide information based on the innovation modules introduced by HEVC. This paper has proposed a high capacity and multilevel information hiding algorithm based on PU partition modes in P-frames, which is considered to be one of the most important innovative features of HEVC. The proposed algorithm consists of two stages: first round calculation and modification process. The first round calculation is to record the PU partition modes selected by HEVC. The modification process is to force each of PU mode into one of the encoded groups according to the embedding binary bits, and then use the modified PU partition modes for the left HEVC encoding process. The experimental results show that the proposed algorithm can achieve high capacity and visual quality, and low bit rate increase, which significantly outperforms state-of-the-art works in embedding capacity.