Coverless Steganography Research Articles

Joint Coverless Steganography and Image Transformation for Covert Communication of Secret Messages

Joint Coverless Steganography and Image Transformation for Covert Communication of Secret Messages

FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning

Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage attacks while ensuring the accuracy of aggregation results, its widespread adoption in blockchain privacy preservation is hindered by the reliance on a trusted key generation center and secure transfer channels. Conversely, coverless steganography schemes effectively ensure the covert transmission of sensitive information across insecure channels. However, their incompatibility with HE‐PPFL arises from the lossy extraction process. To address these challenges, we present a decentralized federated learning privacy‐preserving framework based on the Lifted ElGamal threshold decryption cryptosystem. We introduce a reversible steganography method tailored to safeguard gradient privacy. Furthermore, we introduce a lightweight, secure blind aggregation algorithm founded on the Raft protocol, which serves to protect gradient privacy while substantially mitigating computational overhead. Finally, we provide rigorous theoretical proof of the security and correctness of our proposed scheme. Experimental results from four public data sets demonstrate that our proposed scheme achieves a 100% extraction accuracy without the need for lossless methods, while simultaneously reducing the computational cost of ciphertext gradient aggregation by at least three orders of magnitude. The FedSteg framework is publicly accessible at https://github.com/Xumeili/FedSteg. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain

A review of coverless steganography

With the enhancement of people’s security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review.

Secret data sharing through coverless video steganography based on bit plane segmentation

Recently information hiding is acquiring 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 number of 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 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 is constructed, including the relation between obtained hash sequences and bit-plane features. To begin with, 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.

A Robust Coverless Image Steganography Based on an End-to-End Hash Generation Model

Recently, coverless steganography algorithms have attracted increased research attention due to their ability to completely resist steganalysis algorithms. However, the existing algorithms do not attain the same robust balance against geometric and non-geometric attacks. In addition, most of the existing methods need to transmit some auxiliary information along with the stego-images, which increases the cost of the hidden information. In this paper, a robust coverless image steganography algorithm based on a hash generation model is proposed. Different from the existing methods, the hash sequences are generated by an end-to-end CNN model, where the input is the original images, and the output is the corresponding hash sequences. Therefore, no auxiliary information needs to be transmitted when hiding the secret information. Moreover, the attention mechanism and adversarial training are introduced to improve the robustness of the model. The loss function is redesigned to accommodate these operations. Finally, an index structure is built to enhance the mapping efficiency. The experimental results show that the proposed method possesses better robustness and security compared with the state-of-the-art coverless image steganography algorithms.

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency. Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image. This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation. This system consists of two components. The first component contains two processes, encryption process, and hiding process. The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image. On the other hand, the second component contains two processes, extraction and decryption, which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image, respectively. The results were obtained using the bit error rate (BER) metric. These results confirmed that the system achieved high hiding capacity, good performance, and a high level of robustness against attackers compared with other coverless steganography methods.

A coverless image steganography based on robust image wavelet hashing

Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. In this paper, a new coverless steganography is presented to hide the secret data in a more secure way and to enhance the robustness against attacks. This method depends on frequency domain. The embedding process consists of several steps. Firstly, the secret data is divided into no overlapping segments. Secondly, a set of images is collected to find appropriate images to be stego images. Thirdly, to build a hash sequence for an image, a powerful hashing algorithm is used. Fourthly, for each image hash sequence, the inverted index structure is created. Fifthly, choose the image which its hash equivalent to the secret data segment. Several tests are done to measure the robustness of the proposed method. The results of the experiments reveal that the proposed strategy is resistant to a variety of image processing attacks such as joint photographic experts group (JPEG) compression, noise, low pass filtering, scaling, rotation and median and mean filter, brightness, and sharpening.

A Robust Coverless Steganography Scheme Using Camouflage Image

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrive the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.

A Coverless Text Steganography by Encoding the Chinese Characters' Component Structures

The current coverless text steganography methods have a low steganographic capacity, and yet some of them cannot assure a message can be concealed. How to achieve a high steganographic capacity has become the research hotspot in text steganography. This paper proposes a text coverless steganography method by encoding the Chinese characters’ component structures. Its main idea is that a binary bit string can be conveyed by the Chinese characters’ component structures. The positions of Chinese characters that carry a secret message will be expressed in two systems of the linear remainder equations, whose solutions will be secretly sent to the receiver to extract the secret message. In the method, a single Chinese character can express p bits. The analyses and statistics show that its capacity will be much higher when the same Chinese character is used more than once than existing methods, and it can conceal any message successfully. In addition, this method can also be employed in other languages.

Robust coverless steganography using limited mapping images

Coverless image steganography (CIS) has attracted significant attention because it can fundamentally resist steganalysis tools. Available CIS schemes are mainly divided into synthesis-based and mapping-based schemes. Compared with the former, mapping-based methods ensure lossless information extraction and stronger attack robustness. However, these methods still face the challenges of incomplete secret information mapping, trade-off between robustness and distinguishability of mapping features, and demand for huge numbers of mapping images. To tackle these issues, a novel robust coverless steganography using limited mapping images is proposed in this paper. In our scheme, we extract ring statistics to ensure both the distinguishability and robustness of mapping features. Moreover, different from conventional CIS schemes, we further design a chaotic system for scrambling image features to map secret information. Using this system, a single image is mapped to multiple secret information instances using different scrambled features, which reduces the demand on the numbers of images and avoids incomplete mapping. Furthermore, the scrambled features also enhance the security of secret information. Experimental results demonstrate that our scheme has superior performance in terms of distinguishability and robustness against geometrical attacks, the required number of mapping images, and mapping completeness.

Coverless Video Steganography Based on Audio and Frame Features

The coverless steganography based on video has become a research hot spot recently. However, the existing schemes usually hide secret information based on the single-frame feature of video and do not take advantage of other rich features. In this work, we propose a novel coverless steganography, which makes full use of the audio and frame image features of the video. First, three features are extracted to obtain hash bit sequences, which include DWT (discrete wavelet transform) coefficients and short-term energy of audio and the SIFT (scale-invariant feature transformation) feature of frame images. Then, we build a retrieval database according to the relationship between the generated bit sequences and three features of the corresponding videos. The sender divides the secret information into segments and sends the corresponding retrieval information and carrier videos to the receiver. The receiver can use the retrieval information to recover the secret information from the carrier videos correspondingly. The experimental results show that the proposed method can achieve larger capacity, less time cost, higher hiding success rate, and stronger robustness compared with the existing coverless steganography schemes based on the video.

Survey on deep learning applications in digital image security

In the digital era, sharing pictures on social media has become a common privacy issue. To prevent private images from being eavesdropped on and destroyed, developing secure and efficient image steganography, image cryptography, and image authentication has been difficult. Deep learning provides a solution for digital image security. First, we make an overall conclusion on deep learning applications in image steganography to generate five aspects: the cover image, stego-image, embedding change probabilities, coverless steganography, and steganalysis. Second, we also combine and compare deep learning methods used in six aspects: image cryptography from image compression, image resolution improvement, image object detection and classification, key generation, end-to-end image encryption, and image cryptoanalysis. Third, we collect deep learning methods in image authentication from five perspectives: image forgery detection, watermarked image generation, image watermark extraction and detection, image watermarking attack, and image watermark removal. Finally, we summarize future research directions of deep learning utilization in image steganography, image cryptography, and image authentication.

CSST-Net: an arbitrary image style transfer network of coverless steganography

CSST-Net: an arbitrary image style transfer network of coverless steganography

A Coverless Text Steganography by Encoding the Chinese Characters’ Component Structures

The current coverless text steganography methods have a low steganographic capacity, and yet some of them cannot assure a message can be concealed. How to achieve a high steganographic capacity has become the research hotspot in text steganography. This paper proposes a text coverless steganography method by encoding the Chinese characters’ component structures. Its main idea is that a binary bit string can be conveyed by the Chinese characters’ component structures. The positions of Chinese characters that carry a secret message will be expressed in two systems of the linear remainder equations, whose solutions will be secretly sent to the receiver to extract the secret message. In the method, a single Chinese character can express p bits. The analyses and statistics show that its capacity will be much higher when the same Chinese character is used more than once than existing methods, and it can conceal any message successfully. In addition, this method can also be employed in other languages.

A Big Data Text Coverless Information Hiding Based on Topic Distribution and TF-IDF

Coverless information hiding has become a hot topic in recent years. The existing steganalysis tools are invalidated due to coverless steganography without any modification to the carrier. However, for the text coverless has relatively low hiding capacity, this paper proposed a big data text coverless information hiding method based on LDA (latent Dirichlet allocation) topic distribution and keyword TF-IDF (term frequency-inverse document frequency). Firstly, the sender and receiver build codebook, including word segmentation, word frequency and TF-IDF features, LDA topic model clustering. The sender then shreds the secret information, converts it into keyword ID through the keywords-index table, and searches the text containing the secret information keywords. Secondly, the searched text is taken as the index tag according to the topic distribution and TF-IDF features. At the same time, random numbers are introduced to control the keyword order of secret information.

Coverless Steganography Based on Motion Analysis of Video

With the rapid development of interactive multimedia services and camera sensor networks, the number of network videos is exploding, which has formed a natural carrier library for steganography. In this study, a coverless steganography scheme based on motion analysis of video is proposed. For every video in the database, the robust histograms of oriented optical flow (RHOOF) are obtained, and the index database is constructed. The hidden information bits are mapped to the hash sequences of RHOOF, and the corresponding indexes are sent by the sender. At the receiver, through calculating hash sequences of RHOOF from the cover video, the secret information can be extracted successfully. During the whole process, the cover video remains original without any modification and has a strong ability to resist steganalysis. The capacity is investigated and shows good improvement. The robustness performance is prominent against most attacks such as pepper and salt noise, speckle noise, MPEG-4 compression, and motion JPEG 2000 compression. Compared with the existing coverless information hiding schemes based on images, the proposed method not only obtains a good trade-off between hiding information capacity and robustness but also can achieve higher hiding success rate and lower transmission data load, which shows good practicability and feasibility.

Multimedia Communication Security in 5G/6G Coverless Steganography Based on Image Text Semantic Association

Recently, researchers have shown that coverless steganography is relatively safe. On this basis, to improve the payload of the coverless steganography, a novel semiconstruction coverless steganography algorithm is introduced in the paper. Firstly, web crawler technology is applied to crawl a wide range of small icons and hot news images from the Internet. These icons can be used as the training subset, and the hot news can be designed according to construction rules. Secondly, the Alex-Net network is introduced for training in the algorithm, and the adversarial samples are added to the training set. Thirdly, using the preset template, certain small icons and a hot news image are spliced into a secret carrier image according to the construction principle. The hot news image is in the top half of the carrier, and those small icons are in the bottom half. The image on the upper part of the carrier and the icons of the lower part can be connected by image and text semantics, and the semantic matching can be realized between image semantics and explanatory. The experimental results and analysis show that the proposed algorithm can resist steganalysis tools effectively and has good robustness against various image attacks. Meanwhile, the secret information payload has been greatly improved, the maximum payload can reach 180 bits of a single 512 × 512 image. This promising algorithm can be applied to build covert communications.

Novel Coverless Steganography Method Based on Image Selection and StarGAN

With the development of steganalysis technology keeps progressing, the traditional image steganography has confronted some security bottleneck, as the secret embedding method destroys the naturality of cover image. To overcome this difficulty, cover less image steganography (CIS) has been established for selecting images which contains secrets, and it has received widespread attention due to its merits of persevering the cover image which contains secret information. However, the existing CIS methods have the limitation of being restricted by the size of image database. Up to now, the maximum hidden capacity by CIS is only 18 bits. In order to breakthrough this limitation, this paper proposes a new CIS method based on image selection and Star Generative Adversarial Network (StarGAN). In our scheme, a traditional CIS method is used to select a natural image from database to represent the first part of the secret information, and then an image mapping is established between the rest of the secret information and the face attributes. Finally, the StarGAN is used to construct a high-quality stego image with the mapping relationship. Compared with the existing CIS methods, our method can increase the hidden capacity and maintain a better image quality, while being stronger in terms of robustness and security performance.

Coverless Image Steganography Based on Multi-Object Recognition

Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multi-objects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker’s suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.1\times $ </tex-math></inline-formula> robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.