Steganography Approach Research Articles

Biometric Steganography Using Mid Position Value Technique

Abstract: Biometric data, is prone to attacks and threats from cyber criminals to conduct identity theft, and its economic value makes it a product that can be traded in underground marketplaces such as the dark web. Securing it is the need of the hour. Sensitive data is subsequently in danger throughout a biometric identification system, and therefore the security measures implemented to guard this data must cover all contingencies. A steganographic approach is proposed as a solution to this. Biometrics are hidden inside other biometrics for safe storage and secure transmission. Steganography is a process of hiding data in a transmission medium. The main objectives while hiding data are its undetectability, robustness against image processing and other attacks, which steganography can easily achieve. This project focuses on a process of hiding an image in another image by using mid position value(mpv) technique. Here we have to choose the secret biometric on which Arnold transform will be applied resulting in a scrambled version of the secret biometric. This will be embedded into the cover image resulting in a stego image. Lastly, the hidden secret biometric will be decoded from this stego image, which will first result in a scrambled secret biometric. Inverse Arnold Transform will be applied on this to finally result in the decoded secret biometric.

Image Steganography Approaches and Their Detection Strategies: A Survey

Steganography is the art and science of hidden (or covered) communication. In digital steganography, the bits of image, video, audio and text files are tweaked to represent the information to hide. This article covers the current methods for hiding information in images, alongside steganalysis methods that aim to detect the presence of steganography. By reviewing 456 references, this article discusses the different approaches that can be taken toward steganography and its much less widely studied counterpart. Currently in research older steganography approaches are more widely used than newer methods even though these show greater potential. New methods do have flaws; therefore, more research is needed to make these practically applicable. For steganalysis one of the greatest challenges is the generalisability. Often one scheme can detect the presence of one specific hiding method. More research is needed to combine current schemes and/or create new generalisable schemes. To allow readers to compare results between different papers in our work, performance indications of all steganalysis methods are outlined and a comparison of performance is included. This comparison is given using ‘topological sorting’ graphs, which compares detection results from all papers (as stated in the papers themselves) on different steganographic schemes.

Enhanced Dual-Layer Video Steganographic Approach: Enhancing Security and Imperceptibility

In a digital age where the role of digital information is continually expanding, the imperative to ensure secure communication and data protection has intensified. Steganography, a field that conceals sensitive information within apparently innocuous carriers like multimedia files, holds promise for achieving this goal. Recent times have seen a growing interest in video-based steganography, driven by the proliferation of videos online. However, a review has unveiled a noteworthy gap in efforts to increase the safety of video steganography employing the popular Least Significant Bit (LSB) process. LSB steganography is at risk to exploits that could jeopardize the confidentiality of the concealed message. In this research, we present a video steganographic solution achieved through the integration of advanced encryption algorithms and a dual approach involving randomized frame selection using the Fisher-Yates method and pixel selection utilizing the 8-directional technique. Additionally, we have devised an upgraded imperceptible video steganographic approach in the spatial domain, capitalizing on an XOR-based LSB substitution embedding approach. The outcomes underscore the effectiveness of our proposed methodology, revealing significant imperceptibility when compared to recent solutions, thereby reinforcing data security against unauthorized access. This comprehensive solution holds wide-ranging applications across various domains, providing heightened security and confidentiality for sensitive information. Notable applications include secure communications within intelligence agencies, safeguarding patient data in medical sciences, ensuring privacy in video surveillance, fortifying video transmission against errors and data breaches, securing business communications, preventing data leakage, and enhancing rights management for digital content.

Digital-Signature Oriented Steganography Approach against Man-in-the-Middle Attack

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset.

Image Steganography based on Fernet Symmetric Encryption and Odd-Even Pixel Modification

The combination of steganography and cryptographic approaches for information concealing has piqued the curiosity of numerous researchers. We also integrate those two methods in our research. Using Fernet symmetric encryption and odd-even pixel alteration, this research presents a novel method for image steganography that distributes the data equally throughout the image. To create a stego image, the sender uses a password to encrypt a secret message embedded into an image's red channel. The stego image resembles the original cover image, while the underlying secret data is concealed from human view. The main goals of the system—encryption, consistent data concealing, and message retrieval—were all successfully met. Before integrating the secret data into the image, it can be easily and successfully secured using symmetric encryption using the Fernet cipher. By adding another layer of security to the system, the password feature makes it harder for unauthorized users to access hidden data. By altering image pixels according to whether they are odd or even, the odd-even pixel modification-based steganography approach makes it possible to conceal data. Based on the experiment's results, it can be concluded that this approach embeds concise messages with a high visual quality.

A novel approach to secure communication in mega events through Arabic text steganography utilizing invisible Unicode characters.

Mega events attract mega crowds, and many data exchange transactions are involved among organizers, stakeholders, and individuals, which increase the risk of covert eavesdropping. Data hiding is essential for safeguarding the security, confidentiality, and integrity of information during mega events. It plays a vital role in reducing cyber risks and ensuring the seamless execution of these extensive gatherings. In this paper, a steganographic approach suitable for mega events communication is proposed. The proposed method utilizes the characteristics of Arabic letters and invisible Unicode characters to hide secret data, where each Arabic letter can hide two secret bits. The secret messages hidden using the proposed technique can be exchanged via emails, text messages, and social media, as these are the main communication channels in mega events. The proposed technique demonstrated notable performance with a high-capacity ratio averaging 178% and a perfect imperceptibility ratio of 100%, outperforming most of the previous work. In addition, it proves a performance of security comparable to previous approaches, with an average ratio of72%. Furthermore, it is better in robustness than all related work, with a robustness against 70% of the possible attacks.

Efficient three layer secured adaptive video steganography method using chaotic dynamic systems

In light of the unprecedented growth in internet usage, safeguarding data from unauthorized access has emerged as a paramount concern. Cryptography and steganography stand as pivotal methods for ensuring data security during transmission. This study introduces an innovative adaptive video steganography approach featuring three tiers of security for extracting concealed information, thereby facilitating secure communication. The embedding process operates within the spatial domain of cover video frames, enabling a remarkable hiding ratio of up to 28.125% (equivalent to 2.25 bits per pixel in payload) without compromising the quality of video frames. Users are afforded the flexibility to select between partial or full embedding capacity of CVF through the proposed adaptive control block (ACB). The chaotic key generator (CKG), which combines a logistic map and sine map, is employed to generate highly sensitive initial seeds for permutation order (PO), frame selection (FS), and random position for hiding (RPH), thereby ensuring three levels of security. Prior to transmission, both CVF and hidden data (SD) are encrypted using PO. Encrypted CVFs are then randomly selected using FS for embedding, with RPH employed during the embedding process. Subsequently, for transmitting the stego-video frame, embedded CVFs are decrypted using the same PO. Experimental results demonstrate the efficacy of the proposed approach, achieving an adaptive hiding ratio ranging from 7.1 to 28.125% (equivalent to 0.56 to 2.25 bits per pixel in payload) and maintaining a peak signal-to-noise ratio (PSNR) within the range of 50.25 to 62.05 dB.

Modification in spatial, extraction from transform: Keyless side-information steganography for JPEG

The state of the art steganography approaches strictly assume that the receiver has access to a steganographic key. This limitation was mitigated for images in spacial domain, but the approach does not apply to JPEG images. In this paper, we introduce a keyless steganography scheme for JPEG images. Despite the spatial domain counterpart, our approach for the JPEG domain effectively preserves higher-order statistical models that are used in steganalysis. We show that our approach does not degrade image quality either. The proposed approach is a Side-Information (SI) steganography in the sense that its input is a never-compressed image. Another characteristic of the proposed approach is the separation of the embedding modification and data extraction domains, which can initiate further studies of similar approaches in the future.

An adaptive approach for multi-media steganography using improved chaotic map and discrete cosine transform

An adaptive approach for multi-media steganography using improved chaotic map and discrete cosine transform

Hiding image into image with hybrid attention mechanism based on GANs

AbstractImage steganography is the art of concealing secret information within images to prevent detection. In deep‐learning‐based image steganography, a common practice is to fuse the secret image with the cover image to directly generate the stego image. However, not all features are equally critical for data hiding, and some insignificant ones may lead to the appearance of residual artifacts in the stego image. In this article, a novel network architecture for image steganography with hybrid attention mechanism based on generative adversarial network is introduced. This model consists of three subnetworks: a generator for generate stego images, an extractor for extracting the secret images, and a discriminator to simulate the detection process, which aids the generator in producing more realistic stego images. A specific hybrid attention mechanism (HAM) module is designed that effectively fuses information across channel and spatial domains, facilitating adaptive feature refinement within deep image representations. The experimental results suggest that the HAM module not only enhances the image quality during both the steganography and extraction processes but also improves the model's undetectability. Stego images are mixed with varying levels of noise in the training process, which can further improve robustness. Finally, it is verified that the model outperforms current steganography approaches on three datasets and exhibits good undetectability.

A New Approach of Steganography on Image Metadata

In this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography. The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results.

An Adaptive Steganography Insertion Technique Based on Cosine Transform

In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.

Lossless image steganography: Regard steganography as super-resolution

Image steganography attempts to imperceptibly hide the secret image within the cover image. Most of the existing deep learning-based steganography approaches have excelled in payload capacity, visual quality, and steganographic security. However, they are difficult to losslessly reconstruct secret images from stego images with relatively large payload capacity. Recently, although some studies have introduced invertible neural networks (INNs) to achieve large-capacity image steganography, these methods still cannot reconstruct the secret image losslessly due to the existence of lost information on the output side of the concealing network. We present an INN-based framework in this paper for lossless image steganography. Specifically, we regard image steganography as an image super-resolution task that converts low-resolution cover images to high-resolution stego images while hiding secret images. The feature dimension of the generated stego image matches the total dimension of the input secret and cover images, thereby eliminating the lost information. Besides, a bijective secret projection module is designed to transform various secret images into a latent variable that follows a simple distribution, improving the imperceptibility of the secret image. Comprehensive experiments indicate that the proposed framework achieves secure hiding and lossless extraction of the secret image.

Bio-Inspired algorithms for secure image steganography: enhancing data security and quality in data transmission

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images.

Enhancing Data Security in Multi-Cloud Environments: A Product Cipher-Based Distributed Steganography Approach

Enhancing Data Security in Multi-Cloud Environments: A Product Cipher-Based Distributed Steganography Approach

Secure Data Transfer using Video Steganography

Steganography, a wellestablished practice of covering messages inside standard ones, has found restored significance in the mechanized age, encompassing various mediums like pictures, message, sound, and dynamically, accounts. With the climb of mechanized video correspondence worked with by open taking care of programming, video steganography has emerged as a basic space, wanting to embed data cryptically while staying aware of video quality. This study presents a sharp video steganography plot that unequivocally embeds limited data inside moving things perceived through object recognizable proof, utilizing focus repeat sub-gatherings to safeguard visual decency. Through quantitative and emotional evaluations, the proposed plot shows predominant execution to the extent that intangibility and strength against disturbance attacks, outflanking existing methodologies. Likewise, a flexible steganography approach custom fitted for HEVC accounts is proposed to direct bitrate addition and mutilation storing up. This approach utilizes thought net and PU fragment modes, close by Issue Cross section Code (STC) steganography coding and convolutional cerebrum associations, to overhaul the visual quality and bitrate execution. Preliminary outcomes confirm the feasibility of the proposed computation, showing better perceptual quality through cutting-edgeg-edge strategies. In the end, careful testing against steganalysis techniques upholds the security of the proposed contrive, featuring its significance in working with covert correspondence and security affirmation in electronic video settings

A novel method for linguistic steganography by English translation using attention mechanism and probability distribution theory.

To enhance our ability to model long-range semantical dependencies, we introduce a novel approach for linguistic steganography through English translation. This method leverages attention mechanisms and probability distribution theory, known as NMT-stega (Neural Machine Translation-steganography). Specifically, to optimize translation accuracy and make full use of valuable source text information, we employ an attention-based NMT model as our translation technique. To address potential issues related to the degradation of text quality due to secret information embedding, we have devised a dynamic word pick policy based on probability variance. This policy adaptively constructs an alternative set and dynamically adjusts embedding capacity at each time step, guided by variance thresholds. Additionally, we have incorporated prior knowledge into the model by introducing a hyper-parameter that balances the contributions of the source and target text when predicting the embedded words. Extensive ablation experiments and comparative analyses, conducted on a large-scale Chinese-English corpus, validate the effectiveness of the proposed method across several critical aspects, including embedding rate, text quality, anti-steganography, and semantical distance. Notably, our numerical results demonstrate that the NMT-stega method outperforms alternative approaches in anti-steganography tasks, achieving the highest scores in two steganalysis models, NFZ-WDA (with score of 53) and LS-CNN (with score of 56.4). This underscores the superiority of NMT-stega in the anti-steganography attack task. Furthermore, even when generating longer sentences, with average lengths reaching 47 words, our method maintains strong semantical relationships, as evidenced by a semantic distance of 87.916. Moreover, we evaluate the proposed method using two metrics, Bilingual Evaluation Understudy and Perplexity, and achieve impressive scores of 42.103 and 23.592, respectively, highlighting its exceptional performance in the machine translation task.

Design and Implementation of Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme

Images may be protected from hackers and attackers with the use of steganography. The rapid expansion of the internet has led to the widespread distribution of vast quantities of multimedia content, including photos, movies, and audio files, via various online platforms. To ensure the safety of sensitive information while it is in transit and upon receipt, a high degree of security is required. During the patient scanning procedure, hospitals and scan centers save many pictures of patients on personal computers. Protection from strangers who may see the patients' scanned photos would be necessary for this. Therefore, scan centers and hospitals all over the globe rely heavily on medical image security. The proposed technique includes Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme. To provide the utmost protection for the medical pictures, the cover image incorporates the secret image. At the outset, we standardize the cover and hidden photos. The cover image for this thesis might be a picture of nature or a benchmark; the hidden image, on the other hand, is a medical image in grayscale or binary format. After that, the normalized picture is processed using DWT. The hidden picture is embedded into the cover image using a fuzzy-based edge-related steganography approach, which uses these altered coefficients. To get the stego image, the embedded picture is normalized in the reverse direction. Additionally, this study suggests DT-CWT transform based picture security. Part one of the suggested approach to picture security is image steganography, and part two is picture cryptography. Module 1 uses the DT-CWT transform to fuse the coefficients of the cover picture with the hidden image. After that, the steganography picture is subjected to module 2, which is based on the IE calculation. Analysis of experimental data for the suggested picture security approach revealed improved outcomes for encrypted communication.

Information Security for Audio Steganography Using a Phase Coding Method

The art and science of steganography are dedicated to concealing the presence of a secret message from a third party, such that only the sender and recipient are aware of its content. Various types of media can be used to conceal these communications. When information is hidden in an audio signal, this is referred to as audio steganography. In this paper, two distinct steganography techniques are combined with a multi-level steganography approach: the initial message is embedded in an audio cover at the initial stage, employing, a modified LSB technique, additionally, the second message is embedded in the output from the first level, using a phase coding approach at the second level. A stego audio file is the second level's output containing two audio covers with secret messages. The message is split in multiple ways, with varying proportions between the two levels, in order to investigate how the message's size affects the two procedures used here as well as the levels. The PSNR, MSE, and histogram metrics are used to compare the original and stego audio, in order to assess the effectiveness of the suggested approach. The optimum outcome is achieved when the message is divided in the ratio (1:1). The worst outcome is achieved when the message is divided in the ratio (3:1)

Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network

The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format. The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAho-TACCNet) is introduced for image steganography. Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.