Biblio

Image steganography is the operation of hiding a message into a cover image. the message can be text, codes, or image. Hiding an image into another is the proposed approach in this paper. Based on LSB coding, a k-LSB-based method is proposed using k least bits to hide the image. For decoding the hidden image, a region detection operation is used to know the blocks contains the hidden image. The resolution of stego image can be affected, for that, an image quality enhancement method is used to enhance the image resolution. To demonstrate the effectiveness of the proposed approach, we compare it with some of the state-of-the-art methods.

Information sharing through internet has becoming challenge due to high-risk factor of attacks to the information being transferred. In this paper, a novel image-encryption edge based Image steganography technique is proposed. The proposed algorithm uses logistic map for encrypting the information prior to transmission. Laplacian of Gaussian (LoG) edge operator is used to find edge areas of the colored-cover-image. Simulation analysis demonstrates that the proposed algorithm has a good amount of payload along with better results of security analysis. The proposed scheme is compared with the existing-methods.

Steganography is the concept to conceal information and the data by embedding it as secret data into various digital medium in order to achieve higher security. To achieve this, many steganographic algorithms are already proposed. The ability of human eyes as well as invisibility remain the most important and prominent factor for the security and protection. The most commonly used security measure of data hiding within imagesYet it is ineffective against Steganalysis and lacks proper verifications. Thus the proposed system of Image Steganography using PVD (Pixel Value Differentiating) proves to be a better choice. It compresses and embeds data in images at the pixel value difference calculated between two consecutive pixels. To increase the security, another technique called Edge Detection is used along with PVD to embed data at the edges. Edge Detection techniques like Canny algorithm are used to find the edges in an image horizontally as well as vertically. The edge pixels in an image can be used to handle more bits of messages, because more pixel value shifts can be handled by the image edge area.

Steganography security is the main concern in today’s informative world. The fact is that communication takes place to hide information secretly. Steganography is the technique of hiding secret data within an ordinary, non-secret, file, text message and images. This technique avoids detection of the secret data then extracted at its destination. The main reason for using steganography is, we can hide any secret message behind its ordinary file. This work presents a unique technique for image steganography based on a 512-bit algorithm. The secure stego image is a very challenging task to give protection. Therefore we used the least significant bit (LSB) techniques for implementing stego and cover image. However, data encryption and decryption are used to embedded text and replace data into the least significant bit (LSB) for better approaches. Android-based interface used in encryption-decryption techniques that evaluated in this process.Contribution—this research work with 512-bit data simultaneously in a block cipher to reduce the time complexity of a system, android platform used for data encryption decryption process. Steganography model works with stego image that interacts with LSB techniques for data hiding.

Steganography is the method of hiding one type of information into other type of information, hiding a secret a message in a cover so that others can't know the presence of the secret information. It provides an extra layer of security in communication and information sharing. Security is an important aspect of the communication process; everyone want security in communication. The main purpose of this paper is to introduce security of information that people share among them. In this paper we are presenting different methods of substitution techniques of image steganography and their comparison. Least significant bit and most significant bit substitution techniques are used. Information is hidden in an image file and then decoded back for the secret message. Hiding the presence of any hidden information makes this more secure. This implementation can be used by secret service agencies and also common people for secure communication.

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.

As steganography is the main practice of hidden writing, many deep neural networks are proposed to conceal secret information into images, whose invisibility and security are unsatisfactory. In this paper, we present an encoder-decoder framework with an adversarial discriminator to conceal messages or images into natural images. The message is embedded into QR code first which significantly improves the fault-tolerance. Considering the mean squared error (MSE) is not conducive to perfectly learn the invisible perturbations of cover images, we introduce a texture-based loss that is helpful to hide information into the complex texture regions of an image, improving the invisibility of hidden information. In addition, we design a truncated layer to cope with stego image distortions caused by data type conversion and a moment layer to train our model with varisized images. Finally, our experiments demonstrate that the proposed model improves the security and visual quality of stego images.

There has been a growing expansion in the use of steganography, due to the evolution in using internet technology and multimedia technology. Hence, nowadays, the information is not secured sufficiently while transmitting it over the network. Therefore, information security has taken an important role to provide security against unauthorized individuals. This paper proposes steganography and cryptography technique to secure image based on hybrid edge detector. Cryptography technique is used to encrypt a secret image by using Vernam cipher algorithm. The robust of this algorithm is depending on pseudorandom key. Therefore, pseudo-random key is generated from a nonlinear feedback shift register (Geffe Generator). While in steganography, Hybrid Sobel and Kirch edge detector have been applied on the cover image to locate edge pixels. The least significant bit (LSB) steganography technique is used to embed secret image bits in the cover image in which 3 bits are embedded in edge pixel and 2 bits in smooth pixel. The proposed method can be used in multi field such as military, medical, communication, banking, Electronic governance, and so on. This method gives an average payload ratio of 1.96 with 41.5 PSNR on average. Besides, the maximum size of secret image that can be hidden in the cover image of size 512*512 is 262*261. Also, when hiding 64800 bits in baboon cover image of size 512*512, it gives PSNR of 50.42 and MSE of 0.59.

Video Steganography is an extension of image steganography where any kind of file in any extension is hidden into a digital video. The video content is dynamic in nature and this makes the detection of hidden data difficult than other steganographic techniques. The main motive of using video steganography is that the videos can store large amount of data in it. This paper focuses on security using the combination of hybrid neural networks and hash function for determining the best bits in the cover video to embed the secret data. For the embedding process, the cover video and the data to be hidden is uploaded. Then the hash algorithm and neural networks are applied to form the stego video. For the extraction process, the reverse process is applied and the secret data is obtained. All experiments are done using MatLab2016a software.

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

Now-a-days, video steganography has developed for a secured communication among various users. The two important factor of steganography method are embedding potency and embedding payload. Here, a Multiple Object Tracking (MOT) algorithmic programs used to detect motion object, also shows foreground mask. Discrete wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used for message embedding and extraction stage. In existing system Least significant bit method was proposed. This technique of hiding data may lose some data after some file transformation. The suggested Multiple object tracking algorithm increases embedding and extraction speed, also protects secret message against various attackers.

Part of our team proposed a new steganalytic method based on NIST tests at MMM-ACNS 2017 [1], and it was encouraged to investigate some cipher modifications to prevent such types of steganalysis. In the current paper, we propose one cipher modification based on decompression by arithmetic source compression coding. The experiment shows that the current proposed method allows to protect stegosystems against steganalysis based on NIST tests, while security of the encrypted embedded messages is kept. Protection of contemporary image steganography based on edge detection and modified LSB against NIST tests steganalysis is also presented.

This paper presents an image technique Discrete Wavelet Transform and Singular Value Decomposition for image steganography. We are using a text file and convert into an image as watermark and embed watermarks into the cover image. We evaluate performance and compare this method with other methods like Least Significant Bit, Discrete Cosine Transform, and Discrete Wavelet Transform using Peak Signal Noise Ratio and Mean Squared Error. The result of this experiment showed that combine of Discrete Wavelet Transform and Singular Value Decomposition performance is better than the Least Significant Bit, Discrete Cosine Transform, and Discrete Wavelet Transform. The result of Peak Signal Noise Ratio obtained from Discrete Wavelet Transform and Singular Value Decomposition method is 57.0519 and 56.9520 while the result of Mean Squared Error is 0.1282 and 0.1311. Future work for this research is to add the encryption method on the data to be entered so that if there is an attack then the encryption method can secure the data becomes more secure.

In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. Experimental results show that the new color image steganographic schemes incorporated with the proposed strategy can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

The term steganography was used to conceal thesecret message into other media file. In this paper, a novel imagesteganography is proposed, based on adaptive neural networkswith recycling the Improved Absolute Moment Block TruncationCoding algorithm, and by employing the enhanced five edgedetection operators with an optimal target of the ANNS. Wepropose a new scheme of an image concealing using hybridadaptive neural networks based on I-AMBTC method by thehelp of two approaches, the relevant edge detection operators andimage compression methods. Despite that, many processes in ourscheme are used, but still the quality of concealed image lookinggood according to the HVS and PVD systems. The final simulationresults are discussed and compared with another related researchworks related to the image steganography system.

In this paper a novel data hiding method has been proposed which is based on Non-Linear Feedback Shift Register and Tinkerbell 2D chaotic map. So far, the major work in Steganography using chaotic map has been confined to image steganography where significant restrictions are there to increase payload. In our work, 2D chaotic map and NLFSR are used to developed a video steganography mechanism where data will be embedded in the segregated frames. This will increase the data hiding limit exponentially. Also, embedding position of each frame will be different from others frames which will increase the overall security of the proposed mechanism. We have achieved this randomized data hiding points by using a chaotic map. Basically, Chaotic theory which is non-linear dynamics physics is using in this era in the field of Cryptography and Steganography and because of this theory, little bit changes in initial condition makes the output totally different. So, it is very hard to get embedding position of data without knowing the initial value of the chaotic map.

Steganography is the science of hiding data within data. Either for the good purpose of secret communication or for the bad intention of leaking sensitive confidential data or embedding malicious code or URL. However, many different carrier file formats can be used to hide these data (network, audio, image..etc) but the most common steganography carrier is embedding secret data within images as it is considered to be the best and easiest way to hide all types of files (secret files) within an image using different formats (another image, text, video, virus, URL..etc). To the human eye, the changes in the image appearance with the hidden data can be imperceptible. In fact, images can be more than what we see with our eyes. Therefore, many solutions where proposed to help in detecting these hidden data but each solution have their own strong and weak points either by the limitation of resolving one type of image along with specific hiding technique and or most likely without extracting the hidden data. This paper intends to propose a novel detection approach that will concentrate on detecting any kind of hidden URL in all types of images and extract the hidden URL from the carrier image that used the LSB least significant bit hiding technique.