Cross-domain detection of AI-generated text is a crucial task for cybersecurity. In practical scenarios, after being trained on one or multiple known text generation sources (source domain), a detection model must be capable of effectively identifying text generated by unknown and unseen sources (target domain). Current approaches suffer from limited cross-domain generalization due to insufficient structural adaptation to domain discrepancies. To address this critical limitation, we propose RiDis ,a classification model that synergizes Linguistic Ri chness and Lexical Pair Dis persion for cross-domain AI-generated text detection. Through comprehensive statistical analysis, we establish Linguistic Richness and Lexical Pair Dispersion as discriminative indicators for distinguishing human-authored and machine-generated texts. Our architecture features two innovative components, a Semantic Coherence Extraction Module employing long-range receptive fields to capture linguistic richness through global semantic trend analysis, and a Contextual Dependency Extraction Module utilizing localized receptive fields to quantify lexical pair dispersion via fine-grained word association patterns. The framework further incorporates domain adaptation learning to enhance cross-domain detection robustness. Extensive evaluations demonstrate that our method achieves superior detection accuracy compared to state-of-the-art baselines across multiple domains, with experimental results showing significant performance improvements on cross-domain test scenarios.

Maritime monitoring is crucial in both civilian and military applications, with shore-based radar and visual systems widely used due to their cost-effectiveness. However, single-sensor methods have notable limitations: radar systems, while offering wide detection coverage, suffer from high false alarm rates and lack detailed target information, whereas visual systems provide rich details but perform poorly in adverse weather conditions such as rain and fog. To address these issues, this paper proposes a progressive radar-vision fusion method for surface target detection. Due to the significant differences in data characteristics between radar and visual sensors, direct fusion is nearly infeasible. Instead, the proposed method adopts a stepwise fusion strategy, consisting of coordinate calibration, shallow feature fusion, and deep feature integration. Experimental results show that this approach achieves an mAP50of 86.7% and an mAP75of 54.5%, outperforming YOLOv10 by 1.0% and 1.5%, respectively. Moreover, the proposed method significantly surpasses existing state-of-the-art radar-vision fusion approaches, demonstrating its superior effectiveness in complex environments.

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.

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.