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Steganography, adopted to hide secret information into multiple digital carriers, has been widely used by lawbreakers for spiteful purposes, posing a threat to cyber security. Recently, VoIP speech (compressed speech) has been determined as a suitable carrier for steganography. Among the existing VoIP steganography algorithms, QIM-based VoIP steganography has great stealthiness, making it hard to detect. Although some existing studies have been conducted on detecting QIM-based VoIP steganography, some major challenges still exist that need to be addressed. First, no previous study attempts to estimate the payload locations of QIM-based VoIP steganography, a potential research direction, and a practical way of reducing the search space to recover hidden information. Second, the detection performance of existing models on QIM-based VoIP steganography still has a huge room for improvement. To address the above issues, this paper proposes a novel method called the Steganography Payload-localization Model (SPM) as a countermeasure for QIM-based VoIP steganography. Experimental results show that SPM can estimate the payload locations and perform real-time steganalysis regarding QIM-based VoIP steganography with superb performance. [ABSTRACT FROM AUTHOR]

POSIT is a numerical system consists of sign, regime, exponential, and fraction bits to overcome some limitations of the IEEE‐754 floating point (FP) representation. This work proposes a technique to hide critical information in the least significant bits (LSBs) of the POSIT FP representation by exploiting approximate computing (AC). The proposed technique, called information hiding using POSIT and LSB (IHUPL), explores the opportunities offered by both the POSIT representation and the AC to hide information with a minimum loss in accuracy and other performance metrics. IHUPL offers two options for hiding information: either by embedding one digit of each character into each pixel or by embedding all the digits of the character at the same time into each pixel of the alpha‐red, green, blue, or black/white image. Experimental results are evaluated for benchmark images and showed that IHUPL enhanced the accuracy of embedding data into LSB of POSIT FP by an average of 5%, the image quality improvement rates of IHUPL are 19% and 16% for options 1 and 2, respectively. Besides the encoding method using IHUPL, the paper outlines an extraction decoding technique that saves the original replacement bits in a key‐image to recover the hidden security message. [ABSTRACT FROM AUTHOR]

This research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication. [ABSTRACT FROM AUTHOR]

Image hiding is a task that embeds secret images in digital images without being detected. The performance of image hiding has been greatly improved by using the invertible neural network. However, current image hiding methods are less robust in the face of Joint Photographic Experts Group (JPEG) compression. The secret image cannot be extracted from the stego image after JPEG compression of the stego image. Some methods show good robustness for some certain JPEG compression quality factors but poor robustness for other common JPEG compression quality factors. An image‐hiding network (RIHINNet) that is robust to all common JPEG compression quality factors is proposed. First of all, the loss function is redesigned; thus, the secret image is hidden as much as possible in the area that is less likely to be changed after JPEG compression. Second, the classifier is designed, which can help the model to select the extractor according to the range of JPEG compression degree. Finally, the interval robustness of the secret image extraction is improved through the design of a denoising module. Experimental results show that this RIHINNet outperforms other state‐of‐the‐art image‐hiding methods in the face of JPEG compressed noise with random compression quality factors, with more than 10 dB peak signal‐to‐noise ratio improvement in secret image recovery on ImageNet, COCO and DIV2K datasets. [ABSTRACT FROM AUTHOR]

A recent approach to image steganography is to use deep learning. Mainly, convolutional neural networks can extract complex features and use them as patterns to combine hidden messages and images. Also, by using generative adversarial networks, it is possible to generate realistic and high‐quality stego images without any noticeable artifacts. Previous methods suffered from challenges such as simple architecture, low network accuracy, imbalance between capacity and transparency, vanishing gradients, and low capacity. This study introduces a steganography framework named VidaGAN that utilizes deep learning techniques. The network being proposed is made up of three components: an encoder, a decoder, and a critic, and introduces a novel architecture and several innovations to address some of the unresolved challenges mentioned above. This study introduces a novel method for embedding any type of binary data into images using generative adversarial networks, enabling us to enhance the visual appeal of images generated by the specified model. This neural network called VarIable aDAptive GAN (VidaGAN) achieved state‐of‐the‐art status by reaching a hiding capacity of 3.9 bits per pixel in the DIV2K dataset. Furthermore, examination by the StegExpose steganalysis tool shows an AUC of 0.6, a suitable threshold for transparency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Distrust in voting is not a rare phenomenon even in developed countries. Electronic voting (e-voting), however, appeared as an alternative, but is still not practiced on a large scale. This is due to the fact that despite the huge number of articles it is not yet possible to completely ensure security, privacy and verifiability. It is hard to create a system or a protocol fulfilling all requirements, especially unconditionally. Designing effective voting systems is challenging because these aspects often conflict with each other. There are issues that need to be resolved. For example, one of such challenges is trying to ensure identification and keep votes private while still being able to verify them. There are quite a few cryptographic schemes which fulfill wide requirements for online elections. Their only disadvantage is inconvenience: they use sophisticated cryptographic tools that make them hard to implement and require expertise in various fields. In this paper we suggest a new approach that ensures this requirements without complex cryptographic methods. First, we propose a novel architecture for an internet voting system that incorporates steganography techniques to enhance the security of the system. In the proposed architecture steganography is used to hide the votes within the data packets transmitted between the storage, that keeps all the votes, and the counting server. The next proposed novelty is the solution of the privacy – verifiability problem using only face recognition, properties of image entropy and hash functions. The advantage of this system is ease of use without loss of security. [ABSTRACT FROM AUTHOR]

DNA nanostructures have been regarded as promising platforms for molecular information coding for their high programmability and nanoscale addressability. However, steganography based on DNA nanostructures still needs further investigation. Here, we designed and synthesized a coumarin derivative structure with selective photo responsiveness in the visible light spectrum and developed a DNA origami steganography system that can only be decrypted through specific light exposure conditions. Under right light treatment, the effective cleavage of photoresponsive groups would cause some of the streptavidin binding sites to detach from the origami, thereby allowing the steganographic information to be read correctly. [ABSTRACT FROM AUTHOR]

Recently, implicit neural representation (INR) has started to be applied in image steganography. However, the quality of stego and secret images represented by INR is generally low. In this paper, we propose an implicit neural representation steganography method by neuron pruning. Initially, we randomly deactivate a portion of neurons to train an INR function for implicitly representing the secret image. Subsequently, we prune the neurons that are deemed unimportant for representing the secret image in a unstructured manner to obtain a secret function, while marking the positions of neurons as the key. Finally, based on a partial optimization strategy, we reactivate the pruned neurons to construct a stego function for representing the cover image. The recipient only needs the shared key to recover the secret function from the stego function in order to reconstruct the secret image. Experimental results demonstrate that this method not only allows for lossless recovery of the secret image, but also performs well in terms of capacity, fidelity, and undetectability. The experiments conducted on images of different resolutions validate that our proposed method exhibits significant advantages in image quality over existing implicit representation steganography methods. [ABSTRACT FROM AUTHOR]

Ensuring data security is an essential priority to be put forward once there will be a digital transmission between any two or more targeted audience. Hence, the objective of this paper was to propose a better data security model for a protective information exchange on the internet with steganographic and cryptographic algorithms. Three cryptographic symmetric key encryption algorithm (RC6, Rijndael and TwoFish) and four steganographic carrier object (Image, audio, text and video) were considered. Data obtained show that Rijndael (AES) takes less encipherment and decipherment time compare to RC6 and TwoFish in cryptographic symmetric key encryption algorithm, while image carrier achieves better Peak-Signal-to-Noise Ratio if related to audio, video and text steganography. This research suggests the use of Advanced Encryption Standard and image steganography for efficient information interchange on the internet. [ABSTRACT FROM AUTHOR]

The global growth of the e-commerce industry has heightened concerns among consumers, businesses, and financial institutions regarding fraud while using credit and debit cards and safeguarding personal information. It is necessary to secure information disseminated over insecure channels to prevent unauthorized access. Cryptography and steganography are widely used for this purpose. However, completely relying on the combined usage of both may not be enough in today's world, resulting in weak security. By combining visual cryptography, more levels of security can be added, thus boosting the security of secret information. This study suggests a data security framework that employs multiple levels of security for information. At the first level of security, cryptography is used to encrypt the secret information, and at the second level of security steganography is used to conceal the encrypted text. After that, visual cryptography is applied, which generates the shares of the image obtained. Finally, image steganography is used to hide the generated shares in different color images. When used together, these data security methods greatly improve the secrecy, trustworthiness, and effectiveness of secret messages. The precision of text is determined through Mean Square Analysis (MSE) and correlation coefficient, which involves a comparison of sent and received text. MATLAB environment is used for the implementation. [ABSTRACT FROM AUTHOR]

Accurate tools are required to acknowledge misleading images in order to maintain image legitimacy, and these tools must allow for legal operations on images. Additionally, after posting their images to the Internet, image owners lose rights over the images because there are no measures in place to safeguard them from misuse. One of the most well-liked techniques for addressing copyright disputes is the use of steganography technologies. The embedded steganography images can, sadly, be easily altered or deleted. To address this problem, this work presents the spiking neural network (SNN) with blockchain for tampered image detection utilizing forensic steganography images. Forensic steganography images that have been altered can be found with this SNN. Using steganography images from the database, SNN is trained in this model. The blockchain stores the owners' access policies. The Python platform is used to implement the proposed strategy. F-measure, specificity, accuracy, precision, recall false positive rate (FPR), and false negative rate (FNR) are used to gauge how well the proposed approach performs. When compared to state-of-the-art approaches, the proposed approach obtained an impressive rise of 98.65%, in classification accuracy. [ABSTRACT FROM AUTHOR]

The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse. Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online. Accurately discerning a steganographer from many normal users is challenging due to various factors, such as the complexity in obtaining the steganography algorithm, extracting highly separability features, and modeling the cover data. After extensive exploration, several methods have been proposed for steganographer identification. This paper presents a survey of existing studies. Firstly, we provide a concise introduction to the research background and outline the issue of steganographer identification. Secondly, we present fundamental concepts and techniques that establish a general framework for identifying steganographers. Within this framework, state-of-the-art methods are summarized from five key aspects: data acquisition, feature extraction, feature optimization, identification paradigm, and performance evaluation. Furthermore, theoretical and experimental analyses examine the advantages and limitations of these existing methods. Finally, the survey highlights outstanding issues in image steganographer identification that deserve further research. [ABSTRACT FROM AUTHOR]

Securing communication in our highly digitalized world has become a pressing issue due to the escalating threats of unauthorized data access and violations of network policies. Cryptographic techniques are employed to encrypt data for protection to address these challenges. However, a potential vulnerability arises during data transmission. Sophistic intruders may discern the encrypted information, leading to suspicions and unauthorized access. In response, steganography emerged as an alternative method for communication security. Steganography involves concealing confidential information within the codes of digital files, providing a unique approach that focuses on disguising the presence of communication to enhance data security. In this context, this paper introduces an enhanced information-hiding method implemented by utilizing image edges and modulus functions. This study provides a comparative analysis of various steganographic methods, highlighting the trade-offs between image quality, as evaluated by the Peak Signal-to-Noise Ratio (PSNR), and the payload size. The experimental results indicate that the proposed method has efficient data-hiding capabilities with minimum degradation in the quality of the resulting stego image. [ABSTRACT FROM AUTHOR]

A new steganographic method by pixel-value differencing (PVD) using general quantization ranges of pixel pairs' difference values is proposed. The objective of this method is to provide a data embedding technique with a range table with range widths not limited to powers of 2, extending PVD-based methods to enhance their flexibility and data-embedding rates without changing their capabilities to resist security attacks. Specifically, the conventional PVD technique partitions a grayscale image into 1 × 2 non-overlapping blocks. The entire range [0, 255] of all possible absolute values of the pixel pairs' grayscale differences in the blocks is divided into multiple quantization ranges. The width of each quantization range is a power of two to facilitate the direct embedding of the bit information with high embedding rates. Without using power-of-two range widths, the embedding rates can drop using conventional embedding techniques. In contrast, the proposed method uses general quantization range widths, and a multiple-based number conversion mechanism is employed skillfully to implement the use of non-power-of-two range widths, with each pixel pair being employed to embed a digit in the multiple-based number. All the message bits are converted into a big multiple-based number whose digits can be embedded into the pixel pairs with a higher embedding rate. Good experimental results showed the feasibility of the proposed method and its resistance to security attacks. In addition, implementation examples are provided, where the proposed method adopts non-power-of-two range widths and employs multiple-based number conversion to expand the data-hiding and steganalysis-resisting capabilities of other PVD methods. [ABSTRACT FROM AUTHOR]

Information steganography has received more and more attention from scholars nowadays, especially in the area of image steganography, which uses image content to transmit information and makes the existence of secret information undetectable. To enhance concealment and security, the Steganography without Embedding (SWE) method has proven effective in avoiding image distortion resulting from cover modification. In this paper, a novel encrypted communication scheme for image SWE is proposed. It reconstructs the image into a multi-linked list structure consisting of numerous nodes, where each pixel is transformed into a single node with data and pointer domains. By employing a special addressing algorithm, the optimal linked list corresponding to the secret information can be identified. The receiver can restore the secret message from the received image using only the list header position information. The scheme is based on the concept of coverless steganography, eliminating the need for any modifications to the cover image. It boasts high concealment and security, along with a complete message restoration rate, making it resistant to steganalysis. Furthermore, this paper proposes linked-list construction schemes within the proposed framework, which can effectively resist a variety of attacks, including noise attacks and image compression, demonstrating a certain degree of robustness. To validate the proposed framework, practical tests and comparisons are conducted using multiple datasets. The results affirm the framework's commendable performance in terms of message reduction rate, hidden writing capacity, and robustness against diverse attacks. [ABSTRACT FROM AUTHOR]

The concealment of secret information has become a significant concern in today's highly digitalized world due to the rapid increase in unauthorized data access and network policy violations. In response, steganography has emerged as an alternative technique for securing communication by embedding confidential information within digital files. This paper presents an enhanced scheme for hiding secret bits by utilizing fuzzy-detected edges and a modulus function applied to image pixels in the spatial domain. Unlike previous approaches that focused solely on concealing data in the image’s smooth areas with limited differences, neglecting other potential values, this method addresses these limitations by considering positive and negative difference values between adjacent pixels to hide the secret data effectively. Experimental results show an average improvement of 15% in peak signal-tonoise ratio (PSNR), indicating better stego image quality and a 20% increase in embedding capacity compared to existing benchmark methods. [ABSTRACT FROM AUTHOR]

Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks. [ABSTRACT FROM AUTHOR]

In the realm of machine learning, a discernible surge in research has been observed, focusing on the development of adversarial perturbations with the intent to subvert the capabilities of Deep Neural Networks (DNNs), particularly in the context of object detection and classification. Despite the availability of cutting-edge systems such as the widely acclaimed You Look Only Once (YOLO)v5 model, renowned for its swift image and video classification and detection prowess, our research embarks on a distinctive course exposing the weakness of this detection model and how easily it can be manipulated. This paper seeks to highlight the weaknesses of one of the most advanced neural networks when subjected to carefully crafted adversarial attacks. Our method entails intentionally inserting adversarial perturbations into photos via image-in-image steganography, a technique that is essentially imperceptible to the human eye yet capable of significantly lowering YOLOv5's confidence levels. This approach was carefully, evaluated on a Magnetic Resonance Imaging (MRI) dataset containing around 1100 brain pictures. A comparison between regular and encoded photos undergoing steganography unveiled a substantial decrease in precision values, plummeting from a noteworthy 0.711 to a mere 0.0346. [ABSTRACT FROM AUTHOR]

Several medical consultations and examinations have been undertaken online since the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique that can be used to hide sensitive information within a cover image. This paper presents four improved algorithms to enhance steganography's performance in medical images. A full-size hidden image that is as huge as a cover image cannot be handled by previous methods, which is what the algorithmic design is meant to address. Several creative methods are presented, including the computation of Discrete Cosine Transform (DCT) coefficients based on scaled floating values, the addition of an adaptive compression matrix, and a new approach for systematically dispersing a concealed number of bits across multiple separate locations in the cover image. The results of the experiment showed a notable advancement over the earlier research. Our secret image size is substantially larger than the past studies, yet the structure similarity index matrix (SSIM) of the best reconstructed secret image is close to ideal, the peak signal-to-noise ratio (PSNR) and the payload capacity are higher than in the previous studies. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. [ABSTRACT FROM AUTHOR]

As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation. [ABSTRACT FROM AUTHOR]

Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed "image vaccine" to safeguard digital images from steganography. The process of "vaccinating" an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels. [ABSTRACT FROM AUTHOR]

Steganography technology which conceals a message in a carrier to make it invisible is critical for information security. Conventional optical image steganography using diffractive optical components or spatial light modulators suffers from less encoding channel and bulky volume. The emergence of multifunctional metasurface that can manipulate abundant physical dimensions of optical fields allows the multi‐channel image steganography in a compact volume. Here, the image hiding in a metasurface is demonstrated by modulating the amplitude, phase, and polarization states of terahertz (THz) waves completely. Especially, the phase channel can decouple from the amplitude channel based on a Fresnel‐diffraction‐based algorithm. By directly measuring the phase distribution using the homemade THz focal plane imaging system, the number of transmission channels can expand from N to 2N. As a proof of concept, it is shown that the secret images can encode in the phase channel and subsequently extract by using different keys, such as polarization states, detection distances, and its combinations. Moreover, different hiding strategies with different attacker behaviors are also demonstrated. The decoupling of the phase and amplitude channels with a single metasurface may open an avenue toward innovative optoelectronic devices for optical image steganography, data storage, and terahertz communication. [ABSTRACT FROM AUTHOR]

Steganography refers to the practice of hiding sensitive information inside seemingly unrelated data sets. Steganography in the video is one of the best methods available for hiding data without compromising the film's appearance. For improved security and compatibility, the traditional system uses different video steganography techniques with linear or precise positions. Traditional linear video steganography practices face vulnerability, a lack of security, limited embedding options, and inadequate compatibility. Here nonlinear frame(s) and pixel positions based information hiding techniques have been developed to overwhelm the following. Both the nonlinear frame positions and nonlinear pixel positions are selected for the video‐based steganography. In the beginning, the nonlinear frame positions are selected through the key and the key may be with any prescribed range and alphanumeric characters. A single or more frames may be selected through the key and that entirely depends upon the corresponding run‐through. Then the nonlinear pixel and bit positions are also selected through a similar key. The proposed method is also compared with some former techniques and gives a magnificent result. Furthermore, a security analysis of the suggested algorithm has also been conducted using the differential attack method. To validate the suggested method and ensure that it is accurate, the author of this article made use of a very specific and innovative methodology known as the linguistic response surface methodology (LRSM). This model is framed based on achieving a few steganography assessment measures like PSNR, SSIM, and MSE metric values after incorporating hidden text in various nonlinear frames' nonlinear pixel locations of the video. The analysis of the variance using LRSM for PSNR, SSIM, and MSE response reveals very substantial results with confirmation. [ABSTRACT FROM AUTHOR]

In the modern world, technology has connected to our day-to-day life in different forms. The Internet of Things (IoT) has become an innovative criterion for mass implementations and a part of daily life. However, this rapid growth leads the huge traffic and security problems. There are several challenges arise while deploying IoT. The most common challenges are privacy and security during data transmission. To address these issues, various lightweight cryptography and steganography techniques were introduced. These techniques help secure the data over the IoT. The hybrid of cryptography and steganography mechanisms provides enhanced security to confidential messages. Any messages can be secured by cryptography or by embedding the messages into any media files including text, audio, image, and video using steganography. Hence, this article has provided a detailed review of efficient, lightweight security solutions based on cryptography and steganography and their function over IoT applications. The objective of the paper is to study and analyze various Lightweight cryptography techniques and Steganography techniques for IoT. A few works of literature were reviewed in addition to their merits and limitations. Furthermore, the common problems in the reviewed techniques are explained in the discussion section with their parametric comparison. Finally, the future scope to improve IoT security solutions based on lightweight cryptography and steganography is mentioned in the conclusion part. [ABSTRACT FROM AUTHOR]

Multimedia material, such as digital video, is utilized to conceal a secret message. Given the features of digital video, which has a large storage capacity, confidential data may be inserted. Three requirements must satisfy to grantee secure steganography system. These requirements include security, robustness, and imperceptibility. This paper proposes a steganography scheme to enhance the security of video steganography and attempt to meet the three requirements mentioned above. The security requirement is accomplished through two levels. In the first level, the secret message is encrypted before the embedding process using proposed encryption method based on a combination of chaotic and Arnold's map. In the second level, the secret message is embedded in the selected frames of the video. Instead of traditional LSB technique, we will use a modified LSB technique to meet the robustness requirement. A modified LSB technique is satisfied by embedding the secret message in the LSB of cover video in frequency domain after applying the integer wavelet transform (IWT). According to the experimental results, the stego video quality is like the original video where the obtained PSNR value was 61.922, so the third requirement, imperceptibility, was satisfied. [ABSTRACT FROM AUTHOR]

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte, both for integrity verification and the file checksum of the secret data. This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images. The ACO algorithm uses adaptive exploration to select some pixels, maximizing the capacity of data embedding while minimizing the degradation of visual quality. Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement. The levels of pheromone are modified to reinforce successful pixel choices. Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30% in the embedding capacity compared with traditional approaches; the average Peak Signal to Noise Ratio (PSNR) is 40.5 dB with a Structural Index Similarity (SSIM) of 0.98. The approach also demonstrates very high resistance to detection, cutting down the rate by 20%. Implemented in MATLAB R2023a, the model was tested against one thousand publicly available grayscale images, thus providing robust evidence of its effectiveness. [ABSTRACT FROM AUTHOR]

In today's world, document security has become essential. That stands for either a hard copy or a soft copy of any document. The concept of online, or digital, transactions has become increasingly prevalent in recent years. To accommodate these transactions, there arises a need to transform hard copies of documents into soft copies. This would further lead to a crisis where there is a need to provide security for these soft copies. The documents, when maintained as soft copies, are vulnerable to all sorts of attacks, either through bugs and/or human errors. These human errors are the ones that need to be taken care of on a large scale. Some people believe in sharing documents online with their peers or others of the same interest. This may lead to the misuse of these documents when they fall into the wrong hands. Then, the security of these documents becomes a topic to dwell on. In this paper, a study was conducted based on the ways to secure data and the techniques or algorithms that can be used to do it. There are many different technologies based on the type of data that is being encrypted. These are being discussed along with the papers that were taken into account for various data Security methods. [ABSTRACT FROM AUTHOR]

Background: With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods: To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results: A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability. [ABSTRACT FROM AUTHOR]

Abstract Image hiding is a task that embeds secret images in digital images without being detected. The performance of image hiding has been greatly improved by using the invertible neural network. However, current image hiding methods are less robust in the face of Joint Photographic Experts Group (JPEG) compression. The secret image cannot be extracted from the stego image after JPEG compression of the stego image. Some methods show good robustness for some certain JPEG compression quality factors but poor robustness for other common JPEG compression quality factors. An image‐hiding network (RIHINNet) that is robust to all common JPEG compression quality factors is proposed. First of all, the loss function is redesigned; thus, the secret image is hidden as much as possible in the area that is less likely to be changed after JPEG compression. Second, the classifier is designed, which can help the model to select the extractor according to the range of JPEG compression degree. Finally, the interval robustness of the secret image extraction is improved through the design of a denoising module. Experimental results show that this RIHINNet outperforms other state‐of‐the‐art image‐hiding methods in the face of JPEG compressed noise with random compression quality factors, with more than 10 dB peak signal‐to‐noise ratio improvement in secret image recovery on ImageNet, COCO and DIV2K datasets.

Abstract This research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication.

Abstract POSIT is a numerical system consists of sign, regime, exponential, and fraction bits to overcome some limitations of the IEEE‐754 floating point (FP) representation. This work proposes a technique to hide critical information in the least significant bits (LSBs) of the POSIT FP representation by exploiting approximate computing (AC). The proposed technique, called information hiding using POSIT and LSB (IHUPL), explores the opportunities offered by both the POSIT representation and the AC to hide information with a minimum loss in accuracy and other performance metrics. IHUPL offers two options for hiding information: either by embedding one digit of each character into each pixel or by embedding all the digits of the character at the same time into each pixel of the alpha‐red, green, blue, or black/white image. Experimental results are evaluated for benchmark images and showed that IHUPL enhanced the accuracy of embedding data into LSB of POSIT FP by an average of 5%, the image quality improvement rates of IHUPL are 19% and 16% for options 1 and 2, respectively. Besides the encoding method using IHUPL, the paper outlines an extraction decoding technique that saves the original replacement bits in a key‐image to recover the hidden security message.

Abstract The main goal of image steganalysis, as a technique of confrontation with steganography, is to determine the presence or absence of secret information in conjunction with the specific statistical characteristics of the carrier. With the development of deep learning technology in recent years, the performance of steganography has been gradually enhanced. Especially for the complex reality environment, the image content is mixed and heterogeneous, which brings great challenges to the practical application of image steganalysis technology. In order to solve this problem, we design a forensics aided content selection network (FACSNet) for heterogeneous image steganalysis. Considering the heterogeneous situation of real images, a forensics aided module is introduced to pre-categorise the images to be tested, so that the network is able to detect different categories of images in a more targeted way. The complexity of the images is also further analysed and classified using the content selection module to train a more adapted steganalyser. By doing this, the network is allowed to achieve better performance in recognising and classifying the heterogeneous images for detection. Experimental results show that our designed FACSNet is able to achieve excellent detection performance in heterogeneous environments, improving the detection accuracy by up to 7.14% points, with certain robustness and practicality.

Abstract A recent approach to image steganography is to use deep learning. Mainly, convolutional neural networks can extract complex features and use them as patterns to combine hidden messages and images. Also, by using generative adversarial networks, it is possible to generate realistic and high‐quality stego images without any noticeable artifacts. Previous methods suffered from challenges such as simple architecture, low network accuracy, imbalance between capacity and transparency, vanishing gradients, and low capacity. This study introduces a steganography framework named VidaGAN that utilizes deep learning techniques. The network being proposed is made up of three components: an encoder, a decoder, and a critic, and introduces a novel architecture and several innovations to address some of the unresolved challenges mentioned above. This study introduces a novel method for embedding any type of binary data into images using generative adversarial networks, enabling us to enhance the visual appeal of images generated by the specified model. This neural network called VarIable aDAptive GAN (VidaGAN) achieved state‐of‐the‐art status by reaching a hiding capacity of 3.9 bits per pixel in the DIV2K dataset. Furthermore, examination by the StegExpose steganalysis tool shows an AUC of 0.6, a suitable threshold for transparency.

With the escalating flow of information and digital communication, information security has become an increasingly important issue. Traditional cryptographic methods are being threatened by advancing progress in computing, while physical encryption methods are favored as a viable and compelling avenue. Metasurfaces, which are known for their extraordinary ability to manipulate optical parameters at the nanoscale, exhibit significant potential for the revolution of optical devices, making them a highly promising candidate for optical encryption applications. Here, a single-sized metasurface with four independent channels is proposed for conducting steganography and multi-key information encryption. More specifically, plaintext is transformed into a ciphertext image, which is encoded into a metasurface, while the decryption key is discretely integrated into another channel within the same metasurface. Two different keys for steganographic image unveiling are also encoded into the metasurface and can be retrieved with different channels and spatial positions. This distributed multi-key encryption approach can enhance security, while strategically distributing images across distinct spatial zones serves as an additional measure to reduce the risk of information leakage. This minimalist designed metasurface, with its advantages of high information density and robust security, holds promise across applications including portable encryption, high-camouflaged image display, and high-density optical storage.

Abstract Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed “image vaccine” to safeguard digital images from steganography. The process of “vaccinating” an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels.

Abstract Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks.

With the development of new technologies, 3D models are becoming increasingly important. They are used to design new models, document cultural heritage and scan valuable artefacts or evidence. They are also used in medicine. For these reasons, they are vulnerable to forgery. Protection against forgery done by encrypting the model or signing it digitally may restrict access to the data or require additional files to store the signatures. A good way to confirm the originality of 3D models is fingerprinting. This technique involves attaching a fragile watermark directly to the watermarked data. In the paper, we propose a new fingerprinting method for 3D models. The method hides the fingerprint in the least significant digits of the coordinates of the selected vertices. The fingerprint is created by calculating the hash-based message authentication code (HMAC) from the model textures and all vertex coordinates except the digits intended to attach the fingerprint. These digits are processed using discrete wavelet transform (DWT). The HMAC is attached to the selected DWT coefficients. The inverse discrete wavelet transform is then performed to obtain the new values of the modified digits. The digits are put back into the 3D model coordinates and the model is reassembled. Verification of the model originality is done according to the used steganographic key and consists of comparing the HMAC value extracted from the fingerprinted model with the HMAC value calculated from it. The same values of both HMAC results indicate that the model has not been modified. The proposed method allows efficient model fingerprinting and detection of changes made to any part of the model. The included fingerprints are transparent – the peak signal-to-noise ratio (PSNR) of a fingerprinted model can reach 150dB and its structural similarity can be over 99.8%.

Steganography, the art of hiding information in seemingly harmless digital environments, poses a significant problem for information security. In recent years, deep learning techniques have emerged as powerful tools for a variety of computer vision tasks. This article provides a comprehensive review of the state-of-the-art in deep learning-based steganalysis, focusing on detecting confidential information from the digital environment. In the study, steganographic concepts and their results were investigated, and then different deep learning architectures and methodologies used in steganalysis were examined. The most successful result for test data at 0.2 bpp payload, for all methods, 69% was obtained with the Xu-net model. The most successful result is at 0.4 bpp load, in the WOW method, the VGG16 model was obtained with 86% accuracy. However, since the VGG16 test time is twice as long, the Yedroudj method is seen as the more useful method. Additionally, the study highlights the challenges and limitations of deep learning-based steganalysis and suggests potential avenues for future research. [ABSTRACT FROM AUTHOR]

Traditional encrypted communication technologies have been easily detected and have struggled to meet the needs of secure communication. Steganography, capable of hiding information by modifying the carrier, has been utilized to realize covert communication. However, the potential for steganography to be employed in illegal acts has directed increasing attention towards steganalysis for the detection of steganography, thereby bestowing great research significance upon it. Deep learning has yielded numerous research achievements in fields such as computer vision, pattern recognition, and natural language processing, introducing new opportunities and challenges to steganalysis. These advancements have propelled the generation of new ideas and methods in steganalysis. Currently, color images constitute the mainstream carrier in the process of internet transmission. Nevertheless, existing steganalysis features for color images primarily rely on manual design and often treat the color image as three independent grayscale images, without fully considering the internal relationships between the three color channels, thus necessitating an improvement in detection capabilities for encrypted images. The application of deep learning in the field of color image steganalysis remains in its preliminary stage. The concepts, classifications, and research significance of steganography and steganalysis were introduced, along with an outline of their current research status. Several key techniques for the steganalysis of color images were introduced, compared, summarized, and their development trends were analyzed.

The development of modern linguistic steganography and steganalysis technologies creates new opportunities for use cases to be discovered. Our previous research points to high viability of methods such as trigger-container implementation for the traditional goal of covert information relay. While the findings were significant, it appears that linguistic steganography may have additional applications unrelated to this traditional use case. We aim to analyze how these technologies may be beneficially used in VR-environments, digital governance and for recreational purposes and how these advancements give rise to new speech practices. By investigating the broader implications of linguistic steganography, we hope to uncover innovative ways in which this technology can be harnessed to improve information security, facilitate immersive experiences, and contribute to the development of more sophisticated language-based communication strategies. Using linguistic steganography in VR can improve user experience, ensure sensitive information relay, create new game scenarios. In digital governance these technologies can be used to protect data, ensure secure communications and develop new methods of content analysis. In entertainment, linguistic steganography can be a useful tool for creating riddles, ciphers, and alternative modes of communication in games and other entertainment products. All this gives a new impetus to the development of language practices and prospects for further research in this area.

Communication is an essential aspect of everyday life, involving the transmission of information through various media. Technological advances have made communication easier but have also increased privacy and data security risks. Several efforts are made to maintain the security of digital information, including coding information (cryptography) and hiding information (steganography). In this article, the author secures information through a combination of cryptography and steganography. To secure text data, we encrypt by exchanging bits between adjacent characters. Subsequently, the encrypted text is hidden within an image. The security analysis results show the successful reconstruction of the message from the stego image and the successful restoration of the message to its original form. The use of the bit swapping method in the text message encryption process has been proven to enhance the security level of the ciphertext, as indicated by the lower TPK calculation value of 0.33 compared to the TPK value in previous studies. Additionally, embedding the ciphertext into digital images has been demonstrated to further increase the security level of the message, evidenced by the NPCR calculation value of 0.0000109% and the UACI calculation value of 0.000000555%. These very small values indicate no significant changes.

One of the primary challenges in Internet data transmission lies in safeguarding data from unauthorized access by potential attackers. The goal of content-adaptive steganography is to conceal data inside the image's intricate texture. This research introduces an improved algorithm for concealing messages within color images. The developed method incorporates bit-plane slicing and the RSA algorithm as its foundation, aiming to achieve a heightened level of security for data hiding. The algorithm's uniqueness lies in its adaptability, where the threshold is determined based on both the text and image characteristics. Subsequently, the public key is employed for encryption the thresholds, while the private key is utilized for decryption it. Performance criteria such as Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) are using to assess the quality of the developed algorithm. The results indicate that when 90,000 bits are conceal in the image, it yields an acceptable PSNR value of 60.5749, MSE of 0.0569, and SSIM of 0.9996. The developed algorithm excels in data hiding, as evidenced by its favorable comparison with other studies.