A Novel High-Capacity Behavioral Steganographic Method Combining Timestamp Modulation and Carrier Selection Based on Social Networks

Behavioral steganography is a method used to achieve covert communication based on the sender’s behaviors. It has attracted a great deal of attention due to its robustness and wide application scenarios. Current behavioral steganographic methods are still difficult to apply in practice because of their limited embedding capacity. To this end, this paper proposes a novel high-capacity behavioral steganographic method combining timestamp modulation and carrier selection based on social networks. It is a steganographic method where the embedding process and the extraction process are symmetric. When sending a secret message, the method first maps the secret message to a set of high-frequency keywords and divides them into keyword subsets. Then, the posts containing the keyword subsets are retrieved on social networks. Next, the positions of the keywords in the posts are modulated as the timestamps. Finally, the stego behaviors applied to the retrieved posts are generated. This method does not modify the content of the carrier, which ensures the naturalness of the posts. Compared with typical behavioral steganographic methods, the embedding capacity of the proposed method is 29.23∼51.47 times higher than that of others. Compared to generative text steganography, the embedding capacity is improved by 16.26∼23.94%.

Covert Communication for Wireless Networks with Full-Duplex Multiantenna Relay

In this work, we investigated a covert communication method in wireless networks, which is realized by multiantenna full-duplex single relay. In the first stage, the source node sends covert messages to the relay, and the relay uses a single antenna to send interference signals to the adversary node to protect the covert information being transmitted. In the second stage, the relay decodes and forwards the covert information received in the first stage; at the same time, the relay uses zero-forcing beamforming to send interference signals to the warden to ensure covert transmission. The detection error rate, transmission outage probability, maximum effective covert rate, and other performance indicators are derived in two stages, and the total performance of the system is derived and analyzed. Then, the performance indicators are verified and analyzed by simulation. Our analysis shows that the maximum effective covert rate of using the characteristics of multiantenna to interfere with Willie in the second stage is taken as the total covert performance of the system, and the transmission interruption probability is significantly less than that of the first stage, so the corresponding maximum effective concealment efficiency will be greater.

A Robust MP3 Steganographic Method against Multiple Compressions Based on Modified Discrete Cosine Transform

MP3 appears in various social networking sites wildly, and it is very suitable to be applied for covert communication indeed. However, almost all social networking sites recompress the uploaded MP3 files, which leads to the ineffectiveness of the existing MP3 steganographic methods. In this paper, a robust MP3 steganographic algorithm is proposed with the ability of multiple compressions resistance. First, we discover a new embedding domain with strong robustness. The scalefactor bands of higher energy are applied as the embedding bands. The message bits are embedded by adjusting the position of the MDCT coefficients with the largest magnitude in the embedding bands. Besides, the embedding and extraction operations are realized in the process of MP3 decoding at the same time. Experimental results illustrate that our proposed method is of strong robustness against multiple MP3 compressions. The bit error rate is less than 1% at the MP3 bitrate of 320 kbps. It is worth mentioning that the proposed method is proved to be applicable to social networking sites, such as SoundCloud, for covert communication. Our method achieves a satisfactory level of embedding capacity, imperceptibility, and undetectability.

GENERAL TECHNOLOGY OF ENCRYPTION AND INTEGRATION OF DATA IN A PATTERN OF A PICTURE OF IMAGES THAT IS RESISTANT TO JPEG COMPRESSION

The object of the thesis is the use of steganographic methods for organizing a covert communication channel in a public channel, providing resistance to lossy compression. The aim of the thesis is to develop an algorithm for embedding data into bitmap images that is resistant to JPEG compression and attacks on the container. In this thesis, the features of the JPEG algorithm are investigated, steganographic methods of information protection are analyzed, and a steganographic algorithm is designed that is resistant to JPEG compression and attacks on the container. Additional security is provided by the polyalphabetic substitution cipher and user secret key used to encrypt the original message. The algorithm was developed using the Python 3 programming language, the NumPy, SciPy, MatPlotLib libraries and the Jupyter Lab package. The task was completed using standard mathematical and statistical methods and tools of the high-level programming language Python 3.

Covert Communication Scheme Based on Bitcoin Transaction Mechanism

Due to the unique characteristics of blockchain, such as decentralization, anonymity, high credibility, and nontampering, blockchain technologies have become an integral part of public data platforms and public infrastructure. The communication between the stakeholders of a given blockchain can be used as a carrier for covert communication under cover of legal transactions, which has become a promising research direction of blockchain technology. Due to the special mechanism of blockchain, some traditional blockchain covert communication schemes are not mature enough. They suffer from various drawbacks, such as weak concealment of secret information, cumbersome identification and screening of special transactions, poor availability, and low comprehensive performance. Therefore, this paper designs a scheme of covert communication in the Bitcoin blockchain, which takes normal transactions as a mask and leverages the Bitcoin transaction mechanism to embed secret information in the public key hash field. Specifically, we propose a novel key update mechanism combined with the hash algorithm to construct a covert channel. It ensures security and can update the channel to prevent the related problems caused by address reuse. We are taking advantage of the feature of Bitcoin that cannot be double-spent to solve the problem of burning bitcoin when paying bitcoin to a fake public key hash. In our scheme, both parties to the communication are anonymous, and the attacker cannot detect the covert data or track the transaction and address. Our proposed scheme was tested in real Bitcoin blockchain network, and the experimental results were analyzed to verify its security, availability, and efficiency.

Feature-guided Deep Subdomain Adaptation Network for Dataset Mismatch in Spatial Steganalysis

Steganalysis aims to detect covert communication established via steganography. In recent years, numerous deep learning-based image steganalysis methods with high performance have been proposed. However, these methods tend to suffer from distinct performance degradation when cover images in the train and test set are quite different, also known as cover source mismatch. To address this limitation, in this paper, a feature-guided deep subdomain adaptation network is proposed. Initially, the predictions of the pretrained model are used as pseudo labels to divide the unlabeled samples of the target domain into different subdomains, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterwards, since the steganalysis model may assign incorrect predictions to samples in the target domain, we integrate guiding features to make the division of subdomains more precise. The experimental results show that the proposed network is significantly better than other three networks such as Steganalysis Residual Network (SRNet), deep adaptive network (J-Net) and Deep Subdomain Adaptation Network (DSAN), when it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads. Especially, compared with SRNet, the average accuracy of our method is increased by 5.4% at 0.4bpp and 8.5% at 0.2bpp in the case of dataset mismatch.

The Optimal Carrier-Secret Ratio for Wireless Covert Channels Based on Constellation Shaping Modulation

Wireless covert communication is an emerging communication technique that prevents eavesdropping. This paper considers the bit error ratio (BER) problem of covert communication based on constellation shaping modulation (CSM). The impact of carrier-secret ratio (CSR) on BER is studied and the approximate solution of optimal CSR is obtained. Then, we extended the conclusion to typical communication scenarios with one and more relays where the undetectability and reliability were analyzed and inspected. It is proved that there also exists the optimal CSR in scenarios with relays. Additionally, it is found that the undetectability under the constraints of constant total power depends on the eavesdropper’s position, and we found an undetectability deterioration area (UDA) in the scenario of relays. Simulation results show the existence of optimal CSR and its impact on transmission performance.

Forensic steganalysis for identification of steganography software tools using multiple format image

<p>Today many steganographic software tools are freely available on the Internet, which helps even callow users to have covert communication through digital images. Targeted structural image steganalysers identify only a particular steganographic software tool by tracing the unique fingerprint left in the stego images by the steganographic process. Image steganalysis proves to be a tough challenging task if the process is blind and universal, the secret payload is very less and the cover image is in lossless compression format. A payload independent universal steganalyser which identifies the steganographic software tools by exploiting the traces of artefacts left in the image and in its metadata for five different image formats is proposed. First, the artefacts in image metadata are identified and clustered to form distinct groups by extended K-means clustering. The group that is identical to the cover is further processed by extracting the artefacts in the image data. This is done by developing a signature of the steganographic software tool from its stego images. They are then matched for steganographic software tool identification. Thus, the steganalyser successfully identifies the stego images in five different image formats, out of which four are lossless, even for a payload of 1 byte. Its performance is also compared with the existing steganalyser software tool.</p>