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With the digitalization of information, a lot of multimedia data are under attack, information security has become a key issue of public concern. Image steganography, aiming at using cover images to convey secret information has become one of the most challenge and important subjects in the field of information security recently. Different from the...
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Pixel value differencing (PVD) and least significant bit substitution (LSB) are two widely used schemes in image steganography. These two methods do not consider different content in a cover image for hiding the secret data. The content of most digital images has different edge directions in each pixel, and the local object shape or appearance is m...
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... Compared to other steganographic methods, SSIS stands out for its robustness, security, and preservation of cover image quality. Traditional methods, such as Least Significant Bit (LSB) embedding [29,30] and Discrete Cosine Transform (DCT)-based techniques [31,32], can introduce noticeable distortions in the cover image or suffer from vulnerability to steganalysis. In contrast, SSIS addresses these limitations by leveraging the noise-resistant features of DSSS technology and adaptive spreading sequences. ...
This study investigates the innovative application of Direct Sequence Spread Spectrum (DSSS) technology in the realm of image steganography, known as Spread Spectrum Image Steganography (SSIS). By interpreting the cover image as noise in the communication channel, SSIS capitalizes on the noise-resistant properties of broadband communication systems to effectively conceal information within images. We focus on the development of new classes of spreading sequences with desirable ensemble and correlation properties, which significantly impact the performance of SSIS. We propose a data hiding method that directly addresses spreading sequences, resulting in minimized cover image distortion and heightened resistance to message detection. Furthermore, we explore adaptive spreading sequences that consider the statistical properties of the cover image, substantially reducing error intensity in recovered messages and improving the overall steganographic system performance. Our experiments confirm the advantages of the proposed system and support the theoretical arguments. In addition, we employ artificial neural networks for steganalysis, generating several datasets with varying SSIS payloads and examining the detectability of embedded data using a specially designed convolutional neural network (CNN). While this model demonstrates high effectiveness on other datasets, the detection error probability for SSIS is considerably higher, indicating greater reliability and security even when advanced steganalysis techniques are employed. The findings highlight the potential of SSIS in developing robust and secure communication systems capable of functioning effectively in high-noise environments while preserving the integrity of the cover image.
... As a result, there is a risk of information leakage and weak concealment, jeopardizing the security and robustness of the hidden image. The correlation between the cover and secret images can provide valuable contextual information, which improves the effectiveness of the hiding process [9,10]. Ignoring this correlation limits the ability to achieve high levels of concealment and increases the vulnerability of hidden information to attacks and unauthorized extraction. ...
... • SSIM: The SSIM measures the similarity between two images, that is, the stego image and cover image, and is formulated by Eq. (10). ...
Recently, deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding. However, these approaches have some limitations. For example, a cover image lacks self-adaptability, information leakage, or weak concealment. To address these issues, this study proposes a universal and adaptable image-hiding method. First, a domain attention mechanism is designed by combining the Atrous convolution, which makes better use of the relationship between the secret image domain and the cover image domain. Second, to improve perceived human similarity, perceptual loss is incorporated into the training process. The experimental results are promising, with the proposed method achieving an average pixel discrepancy (APD) of 1.83 and a peak signal-to-noise ratio (PSNR) value of 40.72 dB between the cover and stego images, indicative of its high-quality output. Furthermore, the structural similarity index measure (SSIM) reaches 0.985 while the learned perceptual image patch similarity (LPIPS) remarkably registers at 0.0001. Moreover, self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces, making it suitable for diverse computer vision tasks.
... The private data is slightly modified and designated as a CI by typical image steganography before being embedded into the carrier data. SD and TD-based steganography techniques are the two primary categories of steganography techniques [19][20][21]. ...
Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look. Whereas vehicular ad hoc networks (VANETs), which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services, as they are an essential component of modern smart transportation systems. VANETs steganography has been suggested by many authors for secure, reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection. This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss. According to simulations in literature and real-world studies, Image steganography proved to be an effective method for secure communication on VANETs, even in difficult network conditions. In this research, we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding, statistics, spatial domain (SD), transform domain (TD), distortion, masking, and filtering. This study possibly shall help researchers to improve vehicle networks’ ability to communicate securely and lay the door for innovative steganography methods.
... Trendy the same period, it purposes to ensure commercial continuity with diminishing business harm by limiting the effect of safety incidents. Duplicate steganography, aiming by means of cover images near take secret information has developed one of the greatest challenges as well as vital subjects in the arena of information security freshly [Qin et al. 2019]. Diverse from the old-style image steganography, coverless duplicate steganography does not essential to employment the chosen cover duplicate for implanting the secret data nonetheless directly transmissions secret information finished its own possessions such as pixel illumination value, color, texture, edge, contour as well as high-level semantics. ...
The Most multimedia files, especially those containing private information are images. Since multimedia transmission takes place on public communication channels, it is more vulnerable to a wide range of threats as the internet community evolves. Every day, thousands of people upload and download millions of multimedia files. Steganography, the practice of concealing information inside another data stream so that only intended recipients can access it, is an alluring option for protecting the privacy of data transmissions. Different techniques for embedding and extracting a message from an image file have been suggested in various pieces of study. This thesis proposes a method for improving secret data steganography that utilizes both steganographic and ciphering methods. The suggested method combines steganography with encryption to create a highly secure system, making the data unreadable to hackers.
Encryption of data is commonly used to protect personal information and ensure its veracity and validity. The (Part 1) of the proposed technique introduces a new approach to (encryption, decryption) message encryption and decryption that seeks to preserve message security by increasing the complexity of the encryption key. Stream Cypher Randomization with GA (SCRGA) is the proposed method's official name. Its secretive nature stems from the fact that the input message's statistical features are concealed. In order to make it less likely that the original text may be recovered by statistical analysis or cryptanalysis, the GA raises the rate at which keys are disseminated. Pseudorandom bit generators take a key (k) as input and spit out an unpredictable 8-bit value as output. In order to find the greatest possible match between the input message and the key, the SCRGA takes into account three factors and selects the one with the highest value. Based on an evaluation of the proposed approach to two other state-of-the-art methods, the findings from the tests showed that the SCRGA was superior in terms of both encryption and decryption time to complete as well as encryption key size.
In addition, Text steganography relies heavily on the data concealing capacity and image quality of the cover object, and data encryption is often used to secure in order to ensure secrecy, data privacy. The (Part 2) of a new approach for communication encryption and decryption that uses Genetic Algorithms (GA) and Standard Deviations (STD) to optimize encryption and decryption key complexity is described. The ciphertext is hidden in various sized colour and grayscale images using this method. The confidentiality and privacy of patient information is becoming a major concern for software used in the healthcare industry. The use of a image as a cover for data and information is a kind of steganography represented by images with concealed text. As a result, crucial factors in image masking are the conceal object's image quality as well as its capacity to conceal data. The images include the patient's name, medical history, and any notes the doctor wrote about the patient. Transmission of both image data and text is done in a sequential manner. Standard Deviation (STD) and the Genetic Algorithm (GA) are used to generate a random selection for the optimal cluster. It has been suggested that maximizing and reaping the advantages of similarities across pixels is key. The GA did a great job of concealing the cypher language inside the cover image by dividing it into numerous portions (2 * 2, 4 * 4). Visual quality measures like as PSNR, MSE, Entropy, SSIM, BER, STD, SC, and Histogram are used to evaluate the efficacy, robustness, security, and resistance to typical assaults of the given approach. The goal of a steganographic image is to maintain a high level of image quality while simultaneously increasing the quantity of data to be concealed. The proposed and recommended model has been shown to be effective for masking secret information under a cover image before transmitting it with high efficacy, invisibility, and low future deterioration.
... Basically, coverless steganography is divided into https://doi.org/10.1016/j.jisa.2023.103612 text coverless steganography [9][10][11][12] and image coverless steganography [8,[13][14][15][16][17][18][19][20][21][22][23][24][25]. Unique features such as frequency of words and text keywords are chosen for hiding the secret data in coverless text steganography. ...
Recently, information hiding has acquired significance with the rising multimedia content over the web. Coverless steganography has been perceived as quite possibly the most solid way to deal with moving classified information and has turned into a research problem area. Considering video as a transporter, coverless steganography is expanding prominently because of its greater features than other transporters like text and images. Usually, the existing scheme hides confidential information based on single-frame features in the video. A secret data sharing through a coverless video steganography technique based on bit-plane segmentation has been proposed. After extracting frames from the video, each frame is split into multiple bit-planes using BPCS(Bit-plane complexity segmentation). The hash sequences are obtained from these bit-planes by calculating the mean values of corresponding bit-plane sub-blocks. A retrieval database between obtained hash sequences and bit-plane features is established. At first, the sender altered the secret data into a piece stream and divided it into equivalent lengths. The related retrieval information matching the fragmented secret information is transmitted to the recipient over the insecure channel. The experimental results show that the proposed technique achieves better robustness to various attacks, larger capacity, less time cost for extracting hash sequences, and a higher concealing success rate than existing coverless video steganography techniques.
... In contrast to traditional image steganography, coverless steganography embeds secret data directly into the characteristics of the cover image, such as its edge, texture, pixel brightness value and color, without any designations or alterations. There have been a number of significant contributions [34] in this area. ...
The image transmission over wireless media experiences not only unavailable performance loss caused by the environment and hardware issues, but also information leakage to eavesdroppers who can overhear and attempt to recover the images. This paper proposes a secure cooperative relaying (SCR) protocol for the image communications in wireless relay networks (WRNs) where Alice sends high-resolution (HR) images to Bob with the assistance of a relaying user named Relay, and in the presence of an eavesdropper named Eve. In order to enhance the security of the image communications, random linear network coding (RLNC) is employed at both Alice and Relay to conceal the original images from Eve with RLNC coefficient matrices and reference images in the shared image datastore. Furthermore, the original HR images are downscaled at Alice to save transmission bandwidth and image super-resolution (ISR) is adopted at Bob due to its capability to recover the HR images from their low-resolution (LR) version, while still maintaining the image quality. In the proposed SCR protocol, Bob can decode both the original images transmitted from Alice over the direct link and the images forwarded by Relay over the relaying links. Simulation results show that the SCR protocol achieves a considerably higher performance at Bob than at Eve since Eve does not know the coefficient matrices and reference images used at Alice and Relay for the RLNC. The SCR protocol is also shown to outperform the counterpart secure direct transmission protocol without the relaying links and secure relaying transmission without the direct link. Additionally, an increased scaling factor can save the transmission bandwidth for a slight change in the image quality. Moreover, the impacts of direct, relaying and wiretap links are evaluated, verifying the effectiveness of the SCR protocol with the employment of Relay to assist the image communications between Alice and Bob in the WRNs.
... This technique utilizes face morphing to generate covers without modifying the original images. Coverless image steganography has also been explored using generative adversarial networks (GANs) [28][29][30]35]. GANs are employed to generate covers that hide the secret information while maintaining the visual integrity of the original images. ...
Data hiding refers to the practice of concealing confidential information within seemingly innocent data with an objective to ensure the presence of hidden communication is not easily detected by unauthorized individuals. The traditional technique involves modifying innocent-looking cover media files for embedding the secret message. However, this approach is vulnerable to various statistical attacks that can potentially uncover the hidden data. As a result, alternative methods are explored such as cover selection and cover synthesis, which do not involve modification in original media files and provide better security. The proposed method in the paper involves two main steps: location mapping and cover synthesis. In first step, secret message bits are mapped onto a reference digital media file. This mapping associates specific positions within the media file with the secret message bits. In second step, mapped positions are concealed by synthesizing a model based on the longest common subsequence finding problem. This step aims to hide the mapped positions in a way that extracting the secret message requires knowledge of both the mapping and the synthesized model. The approach claims to provide better privacy protection compared to prior steganography techniques. Additionally, it claims to achieve a 100% accuracy rate in both embedding and extraction processes. It offers a highly resistant covert communication solution and surpasses the existing hiding techniques in terms of security with an aim to address the increasing need for secure communication in the digitized teaching–learning process, especially considering the widespread sharing of study materials over the web.
... To address this problem, coverless image steganography techniques have been developed since 2015, making it possible, to send a message using an image without modifying it [11]. Stego images are thus insensitive to image steganalysis tools that rely on the modification of the cover image. ...
Rather than directly embed the secret message in pixels of an image, the new concept of coverless image steganography hides a message in a cover image without modifying it, which significantly increases the resilience to image steganalysis tools. Existing high capacity coverless image steganography methods do not provide an efficient algorithm for rapidly generating the large number of possible hash sequences. Moreover, the sending of additional information generated during the hiding process and essential for extraction is not explicitly defined. In this paper, we propose WYSAWIS, a high capacity coverless image steganography approach. Given an image initially subdivided into blocks of size 4*4, we generate from each block an initial 15-bit hash sequence, then a set of all the combinations of this sequence. The latter constitutes the set of hash sequences of that block.To send a secret message, it is first subdivided into 15-bit segments,and each segment is mapped to a hash sequence of a block, whose position in the block determines the position of the message segment. All these positions called location information are sent using a distributed data hiding technique in a single cloud. Compared to what has been done in the literature before, our hashing algorithm makes it possible to use a single cover image to send a secret message of any size. This definitely overcomes the limits of building a large image data set and sending in order a large number of stego images. Moreover, the popularization of cloud services makes the transfer of location information innocuous and practical
... Their container images are only randomly sampled from the generative model and cannot be determined by the user. Moreover, existing approaches [45] tend to only involve hiding bits into container images, ignoring the more complex hiding of secret images. Overall, current methods, whether cover-based or coverless, have not been able to achieve good unity in terms of security, controllability, and robustness. ...
... Coverless Steganography. Coverless steganography is an emerging technique in the field of information hiding, which aims to embed secret information within a medium without modifying the cover object [45]. Unlike traditional steganography methods that require a cover medium (e.g., an image or audio file) to be altered for hiding information, coverless steganography seeks to achieve secure communication without introducing any changes to the cover object [31]. ...
Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achieve translation between two images without training, and robustness to noisy data, can be used to improve security and natural robustness in image steganography tasks. For the choice of diffusion model, we selected Stable Diffusion, a type of conditional diffusion model, and fully utilized the latest tools from open-source communities, such as LoRAs and ControlNets, to improve the controllability and diversity of container images. In summary, we propose a novel image steganography framework, named Controllable, Robust and Secure Image Steganography (CRoSS), which has significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. These benefits are obtained without additional training. To our knowledge, this is the first work to introduce diffusion models to the field of image steganography. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed CRoSS framework in controllability, robustness, and security.
... Recently, a new steganographic manner called generative steganography (GS) has attracted researchers' attention [7]. With this approach, secret data is directly converted to natural stego images, i.e., cover images are no longer needed. ...
Generative steganography (GS) is a new data hiding manner, featuring direct generation of stego media from secret data. Existing GS methods are generally criticized for their poor performances. In this paper, we propose a novel flow based GS approach -- Generative Steganographic Flow (GSF), which provides direct generation of stego images without cover image. We take the stego image generation and secret data recovery process as an invertible transformation, and build a reversible bijective mapping between input secret data and generated stego images. In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images. By reversing the mapping, hidden data can be extracted exactly from generated stego images. Furthermore, we propose a novel latent optimization strategy to improve the fidelity of stego images. Experimental results show our proposed GSF has far better performances than SOTA works.
