LSB-based Bit Flipping Methods for Color Image Steganography

Steganography is the art of the hidden data in such a way that it detection of hidden knowledge prevents. As the necessity of security and privacy increases, the need of the hiding secret data is ongoing. In this paper proposed an enhanced detection of the 1-2-4 LSB steganography and RSA cryptography in Gray Scale and Color images. For color images, we apply 1-2-4 LSB on component of the RGB, then encrypt information applying RSA technique. For Gray Images, we use LSB to then encrypt information and also detect edges of gray image. In the experimental outcomes, calculate PSNR and MSE. We calculate peak signal noise ratio for quality and brightness. This method makes sure that the information has been encrypted before hiding it into an input image. If in any case the cipher text got revealed from the input image, the middle person other than receiver can't access the information as it is in encrypted form.

Steganography is one of the methods used for secret communication. Steganography attempts to hide the existence of the information. The object used to hide the secret information is called as cover object. Images are the most popular cover objects used for steganography. Different techniques have to be used for color image steganography and grey scale image steganography since they are stored in different ways. Color image are normally stored with 24 bit depth and grey scale images are stored with 8 bit depth. Color images can hold large amount of secret information since they have three color components. Different color spaces namely RGB (Red Green Blue), HSV (Hue, Saturation, Value), YUV, YIQ, YCbCr (Luminance, hrominance) etc. are used to represent color images. Color image steganography can be done in any color space domain. In this paper color image steganography in RGB and YCbCr domain are compared. The secret information considered is grey scale image. Since RGB is the common method of representation, hiding secret information in this format is not secure.

The process of hiding secret data within a host signal is known as steganography; its design parameters are imperceptibility, concealment capacity, and recovered data quality. A case of images, one of the existing methods based on modification of the host image pixels is called Block Pixel Hiding Method (BPHM), which has good imperceptibility and high-capacity concealment but does not guarantee the quality of the secret image recovered. This article proposes a method that improves results BPHM based on band selection and search algorithm global called Improved Pixel Hiding Method (IPHM). According to the simulations carried out, the results obtained with IPHM are better than those obtained with BPHM. They are similar to one of the more popular methods in imaging steganography known as Quantization Index Modulation (QIM). Steganography is the method of hiding hidden data within a host signal, with imperceptibility, concealment capacity, and retrieved data quality as design criteria. In the case of images, Block Pixel Hiding Method (BPHM) is one of the available methods based on modifying the host picture pixels, which has good imperceptibility and high-capacity concealment but does not guarantee the quality of the hidden picture recovered. Improved Pixel Hiding Method is a method proposed in this article that improves BPHM outcomes by using band selection and a global search algorithm (IPHM). The results obtained using IPHM are better than those achieved with BPHM, according to simulations. They're related to Quantization Index Modulation, which is one of the most widely used picture steganography techniques (QIM).

A technique which enables to have a secret communication in modern technology using public channel is known as steganography This paper deals with constructing and implementing new algorithm based on hiding a large amount of payloads (image, audio, text) file into color image. We have been used adaptive image filtering and adaptive image segmentation with bits replacement on the appropriate pixels. The information has to be hidden in the LSB of the cover image, so there is no much chances present in the embedded image. High security layers have been proposed through three layers to make it difficult to break through the encryption of the input data and confuse steganalysis too. We show that our algorithm can embed efficiently a large amount of data that has been reached to 90% of the image size with high quality of the output. . It is observed that the proposed algorithm has excellent PSNR, provides high level security and more robust compared to individual transform techniques.

Colour and greyscale (black and white) pictures look different to us, but it is not clear whether the difference in appearance is a consequence of the way our visual system uses colour signals or a by-product of our experience. In principle, colour images are qualitatively different from greyscale images because they make it possible to use different processing strategies. Colour signals provide important cues for segmenting the image into areas that represent different objects and for linking together areas that represent the same object. If this property of colour signals is exploited in visual processing we would expect colour stimuli to look different, as a class, from greyscale stimuli. We would also expect that adding colour signals to greyscale signals should change the way that those signals are processed. We have investigated these questions in behavioural and in physiological experiments. We find that male marmosets (all of which are dichromats) rapidly learn to distinguish between colour and greyscale copies of the same images. The discrimination transfers to new image pairs, to new colours and to image pairs in which the colour and greyscale images are spatially different. We find that, in a proportion of neurons recorded in the marmoset visual cortex, colour-shifts in opposite directions produce similar enhancements of the response to a luminance stimulus. We conclude that colour is, both behaviourally and physiologically, a distinctive property of images.

Most existing methods for edge detection rely on contrast dependent thresholds. We show that a local measurement defined by the ratio of the smallest to the largest eigenvalue of the second moment matrix of filter kernels, can be used to separate smooth, low curvature curves and straight lines from noise, independent of contrast, in both color and gray level images. This is done without applying a threshold to the gradient magnitude. The edge images are defined as zero crossings in the gradient direction. The covariance matrix can easily be computed for both gray level images and color images. Further we show the potentiality of such a measure by integrating it with the Hough transform to extract long straight lines in noisy color images. The method is shown to successfully extract consistent line features from color images of a scene, captured under drastically different lightening conditions.

Security of information is becoming main criteria while transferring information through communication networks. Nowadays images are used as information source; therefore its security from unauthorized or unintended access is important. Image encryption play an important role in protecting images from hacking and stealing while sending over an unsecured channel. The proposed method is a combination of encryption using Hill cipher algorithm along with Elliptic curve for enhanced security. Elliptic curve cryptographic method is used to make the system to asymmetric from the less secured symmetric key encryption technique; hill cipher algorithm. In our proposed method we use both grayscale images as well as color images regardless its size. Steganography of actual image with a cover image before encryption is an advantage. The cover image is selected as it mocks like an informative to unauthorized access. Least Significant Bit (LSB) steganography method is used here as it provides more security than any other methods.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

Nowadays, the facilities that offers Internet in conjunction with the capabilities of the infrastructure of communication and information technologies, allowing that the multimedia data can be shared and transmitted in easy and efficient way. As consequence, the multimedia data can be copied or edited in an easy form, and this can be affecting its original content. In a context of digital images, this paper presents a tamper detection and localization algorithm in color images using secure block-based watermarking. This proposal is based on the frequency domain using the lifting wavelet transform, together with checksum criteria that make up the hash key codes, which they are hidden in the least significant bits of each low frequency component. The proposed method is evaluated in terms of imperceptibility and false positive, false negative and tamper detection rates, respectively. Watermark imperceptibility is measured using the peak signal to noise ratio and structural similarity index, respectively. The tamper detection ability has been tested on color and grayscale images using a wide variety of manipulations such as copy-paste, copymove, constant averaging, impulsive noise, and random manipulations. Comparative with the current state-of-the-art is provided.

In this era of information security and communication, a major priority is the achievement of a robust and secure steganography system when thinking about information concealment. The development of such an information-hiding scheme demands that the scheme be able to hide a secret message within the cover media. The most vexing issues in existing steganography protocols are imperceptibility, security, and capacity, and researchers have frequently emphasized a trade-off between these issues. Scholars have consistently ignored the balance between security and payload because resolving one problem has been shown to have an impact on the other, and vice versa. To overcome these problems, an effective method known as the Conventional Neural Network based Edge Detection Method (CNN-EDM) has been presented for image steganography in this study. The CNN-EDM is used to improve the contributions of the proposed scheme. Four main stages were used to achieve the objectives in this research, beginning with the cover image and secret image preparation, followed by embedding, and culminating in extraction. The last stage is the evaluation stage, which employs several evaluations to benchmark the obtained results. A standard database from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 × 512 pixels was utilized in this study. Different parameters were used to test the performance of the suggested scheme based on security and imperceptibility (image quality). The image quality was evaluated using three important metrics: histogram analysis, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, two metrics were used to evaluate the security properties of the proposed system: the Human Visual System (HVS) and Chi-square (X2) attacks. The evaluations showed that the proposed scheme can enhance the capacity, invisibility, and security features and address the already existing problems in this domain.

Digital images are the most common cover files used for steganography. This paper investigates the pros and cons of colour and grayscale versions of images when used as steganography covers. Therefore, it comparatively evaluates the performance of both grayscale and colour versions of a given cover image when they are used with a given steganography method. Moreover, this paper examines the capability and impact of using the chrominance components for data hiding. However, two steganography methods are used as test methods, JSteg and JMQT. As a result, using colour images is better than using grayscale images for data hiding. In order to increase the hiding capacity, chrominance components can be used for data hiding. However, this degrades the quality of stego images and increases the ultimate stego image size.

The method of converting color images from the RGB color system to grayscale images is a simple operation by using the fixed weights method of conversion, but using the same weights to restore the color of the same images is not an effective operation of all types of images because the grayscale image contains little information and it isn't worthy of conversion operation.
 The basic idea in this paper is to employ the mathematics equations which extracted from the grayscale image in conversion operation, this paper presents the method of coloring the grayscale image by using the weights derived from the characteristics of the grayscale image. Skewness, Mean and Standard deviation moments have been extracted from the features of grayscale images and its adoption the determine weights of the RGB color system. This method proved its success in coloring images compared to the traditional method adoption of fixed weights for coloring images because it relies on fixed weights for converting all grayscale images.

In today's era of developed network, it is particularly important to ensure the security of information. Secret image sharing is a kind of secret information protection technology with loss tolerance by sharing a secret image to a number of shadow images. Most of the researches on secret image sharing focus on grayscale images, but the images we use in our daily life are mostly in color, so the research on the secret image sharing for color image is very meaningful and has great application value. The traditional color image sharing scheme is to share and recover R, G, and B color planes separately, which cause that the time of sharing and recovering one color image is at least triple that of sharing and recovering one grayscale image with the same size as the color image. In this paper, we propose an efficient and lossless secret image sharing scheme for color images. We concatenate the R, G, and B values of every color pixel into a long integer and share it by the polynomial-based secret image sharing, thus the sharing efficiency of color images is greatly improved by reducing the number of sharing operations to 1/3 of the traditional scheme. And we achieve lossless recovery by taking GF(16777259) as the finite field of polynomial operations. 16777259 is the smallest prime number greater than 2 24 - 1, so we intend to screen the random numbers to drop the invalid share values. Theoretical analyses and experiments are used to prove the effectiveness of the proposed scheme.

Due to advances in information technology, most of information can be kept electronically, consequently security of information of became a fundamental issues . Steganography is a technique of hiding information in digital media. A large number of commercial steganographic programs use the least significant bit embedding (LSB) as the method of choice for message hiding in 24-bit color images, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, it is proposed to use a LSB embedding algorithm to embed the payload bits of message image into cover image in spatial domain and detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8bit grayscale or color images. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", it can reliably detect messages .By analyzing first order statistic like histogram analysis, entropy it can detect the presence of embedded data. The details of the system and results there of are presented in this paper. (4 pages)

In this article, an advanced image steganographic technique is proposed which may carry large information like audio and video file. It exploits the merit of Advanced Encryption Standard (AES) algorithm which basically generates random pixel position. Here, information become more secure because the algorithm selects the position randomly using AES as well as decide the size of Least Significant Bits for embedding information dynamically. Experiments are conducted to embed audio and video file into a gray scale as well as color images, and results are compared with four state-of-the-art methods, both qualitatively and quantitatively to demonstrate the effectiveness of the proposed method.