A novel minimal distortion-based edge adaptive image steganography scheme using local complexity

Nowadays, due to the rapid technological growth of information technology billions of data transferred through the network around the world in a fraction of seconds. To protect the transferred information from unauthorized access, security mechanism is required. At this point, significant role is performed by a technology called information security. So, to provide security, various techniques have been evolved such as cryptography, steganography and digital watermarking. Cryptography converts the message into scrambled format whereas in steganography the secret message is buried in the digital media. In this paper, both cryptography and steganography is combined to safeguard the information. First, the message is encrypted with the help of revised RSA algorithm to convert the message into secret message. Second, the cover image is segmented into edge and non-edge pixels using Canny edge detection technique and then N1(two) and N2 (four) bits of the secret messages are buried into the edge and non-edge pixel areas using LSB technique. Finally, the essential parameters like N1, N2 and the length values are implanted into the last four pixels of the cover-image to obtain the stego-image. At the recipient side, the hidden data are extracted from the stego-image after extracting N1, N2 and length information. Then, the extracted secret data is decrypted using revised RSA algorithm. Modified RSA algorithm improves the security by using two pairs of keys. At last, the various performance metrics parameters such as entropy, MSE, peak signal to noise ratio and histogram are measured. It is observed that the proposed system attains improved efficiency, security and imperceptibility properties of information hiding from the results.

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.

Due to the effects of quantization error and image noise, detecting ‘meaningful’ line segments from an image with high continuity is a challenging task. To pursue this goal, a novel line segment detector, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contrast-guided line segment detector</i> (CGLSD), is proposed in this paper. Our basic idea is to integrate a low-level image attribute, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge contrast</i> , into the line segment detection process for improving line continuity. After applying an edge detector to the input image, the edge contrast is exploited to guide the growth of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">line-support region</i> for each line segment individually. This is achieved by evaluating edge pixels as well as those non-edge pixels that are nearby the edges. As a result, some of the non-edge pixels are re-considered as ‘edge’ pixels and included for establishing the support region. Reversely, certain edge pixels might be treated as ‘non-edge’ pixels instead and excluded from the region. Since each support region is supposed to yield only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i> line segment, each formed support region needs to have a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">refinement</i> by removing those edge pixels that do not belong to it. Lastly, the support region is required to pass through a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">validation</i> check that might lead to a complete discard of the line segment due to its low confidence. Extensive experiments are conducted and compared with multiple state-of-the-arts on two datasets, including the one from us with manually-annotated ground truth. The results have shown that the proposed CGLSD can deliver superior performance in nearly all test cases.

Security in communication is a top priority in the use of steganography. This concept allows us to convey messages to people we want through a media without arousing suspicion to others. In this case the media used is digital media. More specifically digital media used are image media because images are the most popular media in secret message hiding. The increasingly widespread use of the internet as a communication medium is the reason why this message insertion technique uses the PHP programming language that is currently popular as a programming language supporting web-based applications. The LSB (Least Significant Byte) method is a message hiding technique in steganography where the hidden message is hidden by replacing the data bits in the image segment with secret message bits. Secret message bits are inserted in the low bit or the rightmost bit in the pixel composing the image consisting of red, green and blue (RGB), each of which has an 8-bit value of 0 to 255 with binary format 00000000 to 11111111. Thus, 3 bits of data can be inserted in each pixel image available. The LSB method is then applied in a library which is then named as a stegger which has the function to insert messages in images using the PHP programming language. To add protection to messages sent, an encryption algorithm is added that has been arranged in a class named secrypt that is accompanied by keyword inclusion so that the message sent has a higher level of security. Â Keywords : steganography, PHP, encryption, stegger, secrypt

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels.

Zernike moments (ZMs) are a set of orthogonal moments which have been successfully used in the fields of image processing and pattern recognition. A combination of edge blurring with ZMs computation was introduced. In this study, several kinds of artificial binary stripe images were used to investigate the effects of edge blurring on the absolute mean error of reconstructed image from high-order ZMs. After the blurring process, the reconstruction errors were increased dramatically at edge pixels, but decreased on non-edge pixels. The experimental results demonstrated that 2-pixel blurring approach provided better performance for reducing reconstruction error. Finally, a template matching between two real images was simulated to illustrate the effectiveness of the proposed method.

Canny Edge Detector is the most widely used operator for edge detection. The problem with Canny is that it outputs a binary edge map, where an edge pixel (edgel) is marked (e.g., its value in the edge map is 255) and a non-edge pixel is unmarked (e.g., its value in the edge map is 0). A typical binary edge map is of low quality, consisting of gaps, notch-like structures, ragged and multi-pixel wide edgels. To clean up Canny's binary edge maps, fill up one pixel-wide gaps between the edgels, and to return the map as a set of edge segments, each of which is a one-pixel wide, contiguous chain of pixels, we employ the Smart Routing (SR) algorithm from our recently proposed Edge Segment Detection Algorithm, the Edge Drawing (ED). The proposed algorithm, called Canny Smart Routing (CannySR), runs Canny to obtain a binary edge map, and uses the Canny edgels as anchors for SR to convert them to edge segments. The produced edge segments can then be used in many applications such as line, arc, circle, ellipse, corner detection and other similar higher level object detection applications. We qualitatively evaluate the effectiveness of the proposed algorithm on some sample images and conclude that CannySR visibly improves the modal quality of Canny's binary edge maps although ED seems to produce the best results.

In this paper, we propose a gradient saliency based spatio-temporal video noise reduction method for digital TV. In the proposed method, the gradient saliency, which represents the perceptually distinct image structure, is utilized to accurately identify the edge in a noisy image. The proposed spatial filtering uses the directional Gaussian filter at edge pixels and the isotropic filter at non-edge pixels. In order to suppress the noise in video sequences, we adopt a spatio-temporal structure in which temporal filtering is performed using temporal correlation between two successive frames, and then the temporal filtered result is processed by the proposed gradient saliency noise reduction filter for further noise reduction. The experiment results demonstrate that the proposed method achieves substantial improvements in terms of both visual quality and PSNR as compared with the conventional methods.

Based on the Grey prediction theory, we propose in this paper a two-pass algorithm for the sharpening of images. In the first pass, pixels around edges or boundaries are detected with edge detection mechanism. During the second pass, those pixels detected as around edges or boundaries are adjusted for the purpose of image sharpening, and those non-edge pixels are kept unaltered. With the proposed approach, most of the original information contained in the image can be retained. In the second pass, the magnitude, i.e., the increment or decrement, to be added to those edge pixels has to be determined. Usually, a larger additive can have a better sharpening result. However it can also lead to the saturation of intensity around edge pixels. Aimed to find the maximal additive magnitude automatically, we proposed in this paper the use of a Grey prediction model GM(1,1) so that the condition of over-sharpening in images to be sharpened can be avoided. In addition, a scaling factor can also be used for the adjustment of the additive magnitude in the proposed approach. Extensive experiments on natural images as well as medical images are also given in this paper. As we will see in the experiments, the proposed approach can have a very distinct intensity transition for pixels around edges or boundaries in the sharpened images, which demonstrates the usefulness of the proposed approach.

Audio Steganography is a technique used to transmit hidden information by modifying an audio signal in an imperceptible manner. It is a method that ensures secured data transfer between parties normally in internet community [2]. Here we present a novel approach for resolving the problems related to the substitution technique of audio steganography. In the first level of security, we use an improved RSA encryption algorithm (RPrime RSA) to encrypt message, which is very complex to break. In the next level, the encrypted message is to be encoded into audio data for this we use a more powerful GA (Genetic Algorithm) based Least Significant Bit) Algorithm. In order to increase the robustness against intentional attacks in which the hackers always try to reveal the hidden message as well as some unintentional attacks such as noise addition, the encrypted message bits are embedded into random LSB layers. Here in order to reduce distortion, GA operators are used. The basic idea behind this paper is maintained randomness in message bit insertion into audio data for hiding the data from hackers and to provide a good, efficient method for hiding the data from hackers and sent to the destination in a safer manner [5].

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.

Determining text and non-text regions in an unconstrained handwritten document image is a challenging task. In this article, we propose a novel approach based on entropy for enabling the text line segmentation. A document image is divided into multiple blocks and entropy is calculated for each block. Entropy would be higher in the text region when compared to that of non-text region. Separator points are introduced accordingly to separate text from non-text part. Further correspondence between these separators would enable text line segmentation. The proposed algorithm works with an order of O (m × n) in worst case and requires less buffer space, since it is based on unsupervised learning. Benchmark ICDAR-13 dataset is used for experimentation and accuracy is reported.

Good rate-distortion performance is desired in data hiding, and the theoretical bounds are dependent on the range of allowable modifications made on cover samples. This work proposes a novel data-hiding scheme suitable for various modification ranges, and a family of data-hiding methods with different embedding rates and distortion levels can be generated. In the scheme, the secret digits are represented as a summation of vectors derived from cover samples in a shift manner. By introducing a trellis construction, the distortions of the possible sample-value sequences that can represent the secret data are calculated and only the sequence with the lowest distortion is selected as a new cover. It is shown the rate-distortion performance approaches the theoretical bounds with high embedding rate.

Data hiding is a technique that embeds a cluster of secret messages into the original image. The image with secret messages can be distributed on the Internet while the message embedded would not be easily discovered by a third party. In this way, the secret message can be well protected. At the same time, an image might be made with complex and smooth textures. If changes are made on complex textures, it is less easy for human eyes to discern the differences in the image; but if changes are made on smooth textures, the changes are easier to be discerned by human eyes. This paper proposes a data hiding method based on reduplicated exploiting modification direction (REMD), image interpolation, and canny edge detection. It aims at fulfilling two goals. First, conduct difference-embedding on the image's feature information, distribute the image, and use image interpolation to accomplish reversibility. Second, check the pixels that are located at the edge and insert different data payload according to the application demands. This allows users to flexibly adjust data payload embedded into the image's edge pixels according to their practical conditions, effectively considering both the image quality and payload. The experimental results demonstrate that the proposed method can achieve the data payload of 3.01bpp, so this is a reversible hiding technique with a very high embedding capacity. In the mean time, the average image quality is kept at an acceptable level, about 33?±?1 dB.