Adaptive Image Coding Research Articles

A review of Content Adaptive Image Steganography methods

A review of Content Adaptive Image Steganography methods

Adaptive Image Steganography Using Rotating Color Channels and Inverted LSB Substitution

Adaptive Image Steganography Using Rotating Color Channels and Inverted LSB Substitution

Deep learning approach and cover image transportation: a multi-security adaptive image steganography scheme

ABSTRACT 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.

An adaptive image steganography algorithm based on the use of non-cryptographic hash functions for data extraction

An adaptive steganography algorithm for data hiding in full-color images based on iterative introduction of minor distortions into blocks of container images and the use of high-speed non-cryptographic hash functions for data extraction is considered. Modification of the minimum number of container elements compared to the length of the hidden message is a distinctive feature of the algorithm. This feature allows the hidden throughput to be increased and the visual and statistical visibility of hidden data to be reduced. The algorithm is compared with modern algorithms of adaptive spatial steganography in terms of assessing the level of distorting changes in stego-containers. In addition, a modified version of the algorithm that implements covert channel multiplexing using a common subset of container elements when embedding various messages into them is considered.

An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm

An adaptive image steganography approach depending on integer wavelet transform and genetic algorithm

Constructing Immunized Stego-Image for Secure Steganography via Artificial Immune System

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.

Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.

Seek-and-Hide: Adversarial Steganography via Deep Reinforcement Learning.

The goal of image steganography is to hide a full-sized image, termed secret, into another, termed cover. Prior image steganography algorithms can conceal only one secret within one cover. In this paper, we propose an adaptive local image steganography (AdaSteg) system that allows for scale- and location-adaptive image steganography. By adaptively hiding the secret on a local scale, the proposed system makes the steganography more secured, and further enables multi-secret steganography within one single cover. Specifically, this is achieved via two stages, namely the adaptive patch selection stage and secret encryption stage. Given a pair of secret and cover, first, the optimal local patch for concealment is determined adaptively by exploiting deep reinforcement learning with the proposed steganography quality function and policy network. The secret image is then converted into a patch of encrypted noises, resembling the process of generating adversarial examples, which are further encoded to a local region of the cover to realize a more secured steganography. Furthermore, we propose a novel criterion for the assessment of local steganography, and also collect a challenging dataset that is specialized for the task of image steganography, thus contributing to a standardized benchmark for the area. Experimental results demonstrate that the proposed model yields results superior to the state of the art in both security and capacity.

A Novel Gradient-guided Post-processing Method for Adaptive Image Steganography

Designing an effective cost function has always been the key in image steganography after the development of the near-optimal encoders. To learn the cost maps automatically, the Generative Adversarial Networks (GAN) are often trained from the given cover images. However, this needs to train two Convolutional Neural Networks (CNN) in theory and is thus very time-consuming. In this paper, without modifying the original stego image and the associated cost function of the steganography, and no need to train a GAN, we proposed a novel post-processing method for adaptive image steganography. The post-processing method aims at the embedding cost, hence it is called Post-cost-optimization in this paper. Given a cover image, its gradient map is learned from a pre-trained CNN, which is further smoothed by a low-pass filter. The elements of the cost map derived from the original steganography are projected to 0,1 for separating embeddable and non-embeddable areas. For embeddable areas, the elements will be further screened by the gradient map, according to the magnitudes of the gradients, to produce a new cost map. Finally, the new cost map is used to generate new stego images. Comprehensive experiments have validated the efficacy of the proposed method, which has outperformed several state-of-the-art approaches, whilst the computational cost is also significantly reduced.

Low distortion and adaptive image steganography by enhancing DBSCAN, Sobel operator, and XOR coding

Image steganography is concealing digital messages in an image in a matter that the secret data is not revealed. An effective practice to reduce the variations between the cover and stego images is to hide the secret data in the edge areas. The clustering approach can be applied to detect the edge pixels of the cover image. DBSCAN is a density-based clustering algorithm and is applicable for this purpose. This algorithm, however, may fail to detect edge areas if its parameters are not tuned properly. Furthermore, to reduce the distortion of the stego image, coding techniques should be applied in the data embedding process. The existing coding schemes have low performance and there is still scope for improvement and to propose more promising approaches. In this paper, we propose an effective edge-based image steganography algorithm. The proposed method convolves the cover image using an enhanced Sobel operator prior to edge detection. This approach places more emphasis on the edge pixels. For detecting edge areas, an adaptive clustering method based on the DBSCAN algorithm is proposed, which adjusts its sensitivity against edge pixels according to the cover image structure and message size. As opposed to the traditional DBSCAN, this method is adaptive and can adjust the values of parameters dynamically. Additionally, a novel enhanced XOR coding technique for message concealment which reduces the variations of the edge pixels to 12.5%, is proposed. The experimental results demonstrate that the proposed method improves PSNR and wPSNR by 3.98db and 2.18db compared to other state-of-the-art schemes.

An adaptive image steganography method based on integer wavelet transform using genetic algorithm

Steganography is the science and art of concealing the existence of communication. The idea proposed in this paper is that the coefficients of the high-frequency subbands are more suitable for embedding, as they are surrounded by larger coefficients and represent the image's edge areas. In this method, the appropriate embedding coefficients are selected using an edge intensity criterion based on a local neighborhood analysis. The proposed method begins by blocking the image and then applying a wavelet transform to each block. Several coefficients in each high-frequency subband are identified, and then, using a genetic algorithm, the coefficients are selected so that the generated stego image has the highest PSNR value. The proposed method is more successful when the Integer Wavelet Transform (IWT) is used rather than the Discrete Wavelet Transform (DWT). The proposed method produces a higher-quality stego image than existing methods, and it is secure against steganalysis attacks.

Cost Reassignment for Improving Security of Adaptive Steganography Using an Artificial Immune System

The cost function is crucial to the security of adaptive image steganography, However, some existing cost functions are heuristically designed and hard to be optimal in the undetectability against the evolving steganalyzer. In this letter, we propose a cost reassignment algorithm for adaptive steganography based on artificial immune system. Under the scenario of minimizing additive distortion, this method reassigns the cost by adjusting the modification probability distribution obtained by the cost function, and dynamically optimizes the adjustment mode through the immunity-based information hiding model, so that the modified pixels are more concentrated in the regions that are difficult to be detected. The experimental results show that the proposed method is suitable for a variety of the state-of-the-art cost functions and can achieve better performance on resisting the steganalysis.

Steganography Schemes for Data & Information Protection

In this digital world of data transmission, information is at the heart of computers, transactions and international economic dealings. In ensuring the security of data and information researchers have come up with different schemes and techniques that can provide protection for data and information during transmission. The protection of sensitive information involves many interdependent policies and technological issues relating to information confidentiality, integrity, authenticity, anonymity, and utility. In this paper, we review literature on steganography protection schemes and highlight several developments that has ensured intellectual property protection over time. Keywords: Watermarking, Steganography, Fingerprinting, Cryptography, Intellectual Properties, Protection, Concealment, Adaptive Image Steganography

Steganography Schemes for Data & Information Protection

In this digital world of data transmission, information is at the heart of computers, transactions and international economic dealings. In ensuring the security of data and information researchers have come up with different schemes and techniques that can provide protection for data and information during transmission. The protection of sensitive information involves many interdependent policies and technological issues relating to information confidentiality, integrity, authenticity, anonymity, and utility. In this paper, we review literature on steganography protection schemes and highlight several developments that has ensured intellectual property protection over time. Keywords: Watermarking, Steganography, Fingerprinting, Cryptography, Intellectual Properties, Protection, Concealment, Adaptive Image Steganography

An Android-Based Image Steganography System for Concealing Data and Information Transmission Using Adaptive Image Steganography

The growth in the volume of information transmission and transaction over the internet has necessitated the development and enhancement of measures and techniques required to ensure the safety of critical information during transmission. with developments in mobile technologies and the adoption and diffusion of android mobile devices, research must therefore continue to explore more secured media in which information can be hidden. One of such medium is the use of image carriers referred to as steganography. In tis paper, we provide insughts into the development of an android-based Image Steganography System for concealing data and information using adaptive image steganography. The Least Significant Bit (LSB) algorithm was employed as the algorithmic sequence for the development. Preliminary results from implementation and testing showed promising results Keywords: Android, image steganography, data, information, transmission, images, security

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

The advantage of spatial domain image steganography techniques is their capacity to embed high payloads of data by directly modifying image pixels. While these techniques have a high-embedding capacity, they often create visual and statistical distortion in smoother regions. Most existing edge steganography techniques divide an image into blocks and insert data by processing the blocks in a linear order, but these method also has multiple drawbacks. First, if the selected block has an insufficient number of edge pixels, it may result in multiple blocks being processed. Second, at high embedding rates, the method creates severe distortion as multiple message bits are hidden in edge pixels and surrounding non-edge pixels without analyzing the statistical dependencies and correlation of pixels, compromising data security. The aim of the proposed method is to construct a Block-wise Edge Adaptive Steganography Scheme (BEASS) using textured regions, particularly edges and surrounding pixels. This scheme dynamically chooses the region to embed messages using a local complexity measure of Standard Deviation. It offers high payload, minimal distortion embedding by hiding three message bits into edge pixels using the minimal Mean Square Error to determine the embedding capacity of neighboring non-edge pixels within the block to preserve the statistical dependencies. The practical merit of this approach was validated and compared with existing algorithms, and experimental results find that the proposed method surpasses IQM tests, achieves a high PSNR of61 $\sim $ 65, proves to be robust against kurtosis and skewness distortion, resists histogram attack, RS steganalysis and high dimensional ensemble classifier at 80% block modifications.

ABMC-DH: An Adaptive Bit-Plane Data Hiding Method Based on Matrix Coding

This paper proposes a novel adaptive image steganography method combining matrix coding, which can achieve better visual quality according to the given embedding rate compared with other existing schemes. The embedding processes are divided into two phases: preparation and implementation. In the preparation phase, all 128 combinations of a 7-bit binary number are classified into eight groups by a parity check matrix based on matrix coding. Thus, each group corresponds to an octal number, and contains 16 candidates. Correspondingly, all pixels of a cover image are classified into three categories by a predefined embedding rate level: low, middle, and high. In the implementation phase, each pixel in the high level can be embedded with 3 bits by substituting its 7 LSBs (Least Significant Bit) using a close candidate from the group determined by the to-be-embedded 3 bits. For the middle level, pixel pairs are used to hide 3-bit data by substituting its (3, 4) LSBs combination using a close candidate form the group determined by the to-be-embedded 3 bits. For pixels in the low level, a triple pixel combination is used to embed 3-bit data by substituting its (2, 2, 3) LSBs combination using a close candidate form the group determined by the to-be-embedded 3 bits. The experimental results indicate that the proposed scheme is optimal compared to other existing matrix-coding data hiding schemes in terms of visual quality, while providing an identical embedding capacity.

High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform

The importance of image steganography is unquestionable in the field of secure multimedia communication. Imperceptibility and high payload capacity are some of the crucial parts of any mode of steganography. The proposed work is an attempt to modify the edge-based image steganography which provides higher payload capacity and imperceptibility by making use of machine learning techniques. The approach uses an adaptive embedding process over Dual-Tree Complex Wavelet Transform (DT-CWT) subband coefficients. Machine learning based optimization techniques are employed here to embed the secret data over optimal cover-image-blocks with minimal retrieval error. The embedding process will create a unique secret key which is imperative for the retrieval of data and need to be transmitted to the receiver side via a secure channel. This enhances the security concerns and avoids data hacking by intruders. The algorithm performance is evaluated with standard benchmark parameters like PSNR, SSIM, CF, Retrieval error, BPP and Histogram. The results of the proposed method show the stego-image with PSNR above 50 dB even with a dense embedding of up to 7.87 BPP. This clearly indicates that the proposed work surpasses the state-of-the-art image steganographic systems significantly.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

An adaptive color image steganography method using adjacent pixel value differencing and LSB substitution technique

Steganography is a data-concealing technique that provides a covert communication. This paper proposes a spatial domain color steganography method that uses a neighboring pixel-pair differencing concept to enhance the embedding capacity. The proposed method considers the distortion tolerance capacity of each color plane and the difference value between two neighboring pixels while deciding the number of bits that can be concealed in that pixel-pair. Each plane is divided into some disjoint blocks of size 3 × 3 pixels. The method forms eight pairs of pixels in one block using the eight-neighboring pixel concept, which enables the method to utilize all the edge pixels in all probable directions. It also uses the XOR encoding technique before the embedding process. This step provides an additional layer of security to the secret information. The simulation results show that the proposed method is able to achieve a high embedding capacity with good visual quality as per the calculation of imperceptibility metrics. The robustness of the method is tested by RS steganalysis and pixel difference histogram analysis. Further, a comparison between the proposed method and the existing methods on the basis of embedding capacity and stego image quality is shown.