In scenarios where steganographic texts from various steganographic domains generated by different generative steganography algorithms are mixed, most existing linguistic steganalysis methods lack corresponding network structures designed to account for the differences in steganographic texts from different domains, leading to the potential for further improvement in their general detection performance. To address the above issue, we propose a general generative linguistic steganalysis method based on the basic idea of dynamically extracting lexical association features of different steganographic domains at the segment level. We utilize dynamic-static text feature matrix to construct a word importance semantic encoding module to mine steganography-sensitive word features of different steganographic domains. Based on the obtained features, we propose a word correlation multi-scale perception module to focus on the segment-level lexical association changes caused by secret information embedding in different domains. Experimental results show that this method can improve the detection accuracy of existing mainstream linguistic steganalysis methods in various mixed steganography scenarios.

Current researchers have proposed various steganalysis methods for detecting secret information within social media texts, which can achieve relatively optimal detection performance in specific steganographic domains. However, considering the practical application of social media, we can only obtain the text to be tested without prior knowledge of the steganographic domain it belongs to. Consequently, we are unable to prepare a supervised training dataset in advance. This places higher demands on steganalysis algorithms, necessitating their ability to generalize and detect any unknown steganography domain. To this end, we propose a universal detection method for generative linguistic steganography based on heterogeneous domain remapping. The core idea is to employ a neural structure composed of pre-trained embedding layers and capsule networks to extract steganography-sensitive correlation features. Subsequently, the concept of contrastive learning is utilized to remap the sensitive features from heterogeneous steganography domains into a unified domain. This process effectively extracts domain-invariant features, thereby enabling the detection of unknown steganographic domains. Experimental results demonstrate that the proposed method outperforms existing approaches by an average of over 2% across various steganography domains.

Radar target detection is an important technical means for effective monitoring of sea surface targets. Under strong sea clutter, the echo characteristics of some targets with small radar cross-sections (RCS) may be submerged, affecting the performance of the detector, such as CFAR (Constant False Alarm Rate). The importance of sea clutter suppression as one of the preprocessing steps for detection is self-evident. In this letter, we propose a two-stage collaborative sea clutter suppression algorithm based on subregion gazing and reconstruction, which combines data-driven neural models and vector factorization theory to first magnify the subregions where targets may exist in the echo, and then use vector factorization methods to suppress clutter and noise within the echo reception window. To verify the effectiveness of the proposed algorithm, we conducted long-term observations of several channel buoys in the southern waters of China, forming an effective dataset. Experimental results on this dataset demonstrate that, by employing the sea clutter suppression method proposed in this paper as a data preprocessing technique, the F1-score of the detection algorithm can average over 56.81%.

Most of the existing steganalysis methods for low-bit-rate compressed speech are specifically designed for a particular speech encoder or category of steganography methods, limiting their generalization capability. These methods require pre-selection of codewords affected by the specific steganographic process as input to the steganalysis models. In order to overcome this limitation and enhance the practicality of steganalysis algorithms, we propose a compressedSpeech encoder and steganographyAlgorithm independent steganalysisNetwork, namedSANet. Irrespective of the specific steganography algorithm used, modifications to the codewords will impact the sequential correlation characteristics of uncompressed domain (time domain) speech. Additionally, the compressed speech streams from different coders are unified in the uncompressed domain format. Therefore, this article introduces an intermediate representation based on the uncompressed domain and develops a neural network that utilizes collaborative correlation features to extract steganography-sensitive characteristics from this representation. Experimental results demonstrate that our proposed method achieves state-of-the-art detection performance for various steganography algorithms under different speech encoders.

Radar target detection in sea clutter aims to effectively discern the presence of maritime targets within the current radar echo. With the advancement of deep learning technology, an ever-growing number of researchers are turning to neural networks as the foundation for constructing detection models. These sophisticated neural models demonstrate promising performance in public datasets. On this basis, we propose an innovative self-evolution framework for radar target detection models using semisupervised learning (SesuL). The proposed approach aims to enhance the performance of the detection model under various radar conditions. Notably, this research presents the first-ever attempt within the literature to introduce such an approach for pulse-compression radar. To bolster the performance of the proposed model, novel techniques are introduced for sample selection, sample augmentation, and model optimization. Experimental findings provide compelling evidence supporting the superiority of the proposed method in terms of detection performance and robustness under unknown conditions, surpassing existing techniques. In light of practical deployment considerations, future efforts should be directed toward investigating the fusion of radar and other sensors, such as visible light, to enhance the detection performance.

Sea surface object detection plays an important role in the coastal defense monitoring system. Existing target detection methods mostly lack the adaptive perception of background changes. In addition, these methods fail to further integrate and interact with the multi-layer features extracted from the deep backbone network. To address these two issues, we first propose a Background Dynamic Perception module, which uses environmental information as an auxiliary. We train the detector to dynamically capture the background changes through a multi-task learning framework. Moreover, we propose a Cross-Layer Semantic Interaction module, which can achieve cross-layer interaction and reduce information loss. Based on the above modules, we propose a sea surface object detection network. To verify the performance, we collected real sea surface data and built a sea surface object dataset. Experimental results demonstrate that our method achieves 74.4% AP on the dataset, outperforming the latest methods.

Radar-based maritime target detection plays an important role in ocean monitoring. Considering the practical application, pulse-compression radar is widely used in terms of civilian offshore surface target detection. The existence of sea clutter will greatly interfere the detection performance of pulse-compression radar. This leads to the low detection performance of traditional algorithms like constant false alarm rate (CFAR). Deep learning methods have made strides in many fields recently, such as natural language processing and speech recognition. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on convolution neural network (CNN) and dual-perspective attention (DPA). The proposed method first encodes the radar echo in high-dimensional space and then extracts the correlation features from the global and local perspectives through the attention mechanism. We deployed the X-band pulse-compression radar on the coast of Hainan, China, and collected a lot of measured data. Experimental results demonstrate that the detection performance of our method outperforms the traditional CFAR methods and the latest deep learning-based methods. In the measured dataset, our proposed method can reach a detection probability of 93.59% under a false alarm rate (FAR) of$1e-3$, reaching the practical application level.

Covert communication channels can be easily constructed using text steganography based on social media. Offenders can easily utilize these channels to engage in various criminal activities, which brings great challenges in maintaining the security of cyberspace. Among the text information hiding methods, generative linguistic steganography poses the biggest threat to network security because it does not need the original carrier and has high embedding efficiency. The existing generative linguistic steganalysis methods fail to deeply mine text word relation, hence the detection performance is relatively unsatisfactory. In this article, we prove that there is explicit and latent steganography-sensitive text word relation. Based on this, we propose a generative linguistic steganalysis method based onExplicit andLatent text word relationMining, namedELM. First, we employ a distributed readin module to convert words into real number vectors. Then, MRA (Mining Relation by Attentions) is proposed to mine the explicit and latent text word relation. Finally, global adaptive classification module is presented to exploit the mined relation feature to predict whether secret information is embedded in the current text segment. Experimental results demonstrate that the detection performance of ELM is better than the existing generative linguistic steganalysis methods.

Frame-wise steganalysis is of significance for active steganography defense. By frame-wise detection, we can accurately find the embedding position of secret information and destroy the covert channel further. However, there is currently no research specifically aiming at frame-wise steganalysis of low-bit-rate compressed speech. Besides, most of the existing steganalysis methods are specifically designed for a specific category of steganography methods. They are difficult to apply to practical scenarios where the steganography algorithms are uncertain. In this paper, a general frame-wise steganalysis method for low-bit-rate compressed speech is proposed. To extract rich feature from a speech frame, we propose a dual-domain representation, which conducts feature extraction both in the compressed domain and the decoded time domain. In addition, we propose an efficient steganalysis network named Stegaformer to leach the intra-frame correlation from the obtained representation to enable steganalysis. In Stegaformer, an adaptive local correlation enhancement module is introduced to effectively models the local characteristics, which compensates for the drawback of traditional Transformer-based models. Experimental results show that our method performs better than the existing steganalysis methods in detecting multiple steganography methods for a speech frame.

Steganographic algorithms in low-bit-rate compressed speech bring convenience to realize covert communication, meanwhile result in safety issues. The existing steganalysis methods are normally designed for one specific category of steganographic methods, thus lacking generalization capability. In this paper, we propose a general steganalysis method based on code element (CE) embedding, Bi-LSTM and CNN with attention mechanisms. Firstly, CEs in each frame are converted to a multi-hot vector. And each multi-hot vector will be mapped into a fixed-length embedding vector to get a more compact representation by utilizing dictionaries. Then, Bi-LSTM and CNN are applied to extract the contextual information and the local characteristics respectively of these embedding vectors. In addition, the attention mechanisms are introduced in different layers of the network to assign different weights to the output feature within each layer. Finally, the prediction results can be generated by the fully connected layer. Experimental results show that our method performs better than the existing steganalysis methods for detecting multiple steganography methods in the low-bit-rate compressed speech streams.

Recent progress in vision-based fire detection is driven by convolutional neural networks. However, the existing methods fail to achieve a good tradeoff among accuracy, model size, and speed. In this paper, we propose an accurate fire detection method that achieves a better balance in the abovementioned aspects. Specifically, a multiscale feature extraction mechanism is employed to capture richer spatial details, which can enhance the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism is utilized to enhance the interaction among information flows through dense skip connections. Finally, a channel attention mechanism is employed to selectively emphasize the contribution between different feature maps. Experimental results demonstrate that our method achieves 95.3% accuracy, which outperforms the suboptimal method by 2.5%. Moreover, the speed and model size of our method are 3.76% faster on the GPU and 63.64% smaller than the suboptimal method, respectively.

Steganalysis of the quantization index modulation (QIM) steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization (VQ) codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network (QCCN) based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine (SVM) classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.

This paper presents a method to detect the quantization index modulation (QIM) steganography in G.723.1 bit stream. We show that the distribution of each quantization index (codeword) in the quantization index sequence has unbalanced and correlated characteristics. We present the designs of statistical models to extract the quantitative feature vectors of these characteristics. Combining the extracted vectors with the support vector machine, we build the classifier for detecting the QIM steganography in G.723.1 bit stream. The experiment shows that the method has far better performance than the existing blind detection method which extracts the feature vector in an uncompressed domain. The recall and precision of our method are all more than 90% even for a compressed bit stream duration as low as 3.6 s.