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.

QIM(quantization index modulation)-based VoIP steganography is an information-hiding technology that malicious users could misuse to engage in illegal activities. Its countermeasure, commonly known as the QIM-based VoIP steganalysis, has been one of the research hotspots over the past decades. VoIP speech data is sequential, so most previous studies have focused on the temporal features extracted from VoIP encoding codewords to improve detection accuracy. As a result, spatial features are often ignored or fully investigated. In practice, VoIP speech data has unique spatial characteristics. Spatial features could capture the properties of nearby codewords and frames. Inspired by the success of CLIP (contrastive language–image pre-training), we propose a novel model that could efficiently incorporate spatial and temporal features named CAST (contrastive analysis of spatial and temporal features). CLIP introduces the concept of contrastive language–image learning, which has demonstrated exceptional efficiency and effectiveness in aligning textual and visual representations. However, it is not directly applicable to the field of QIM-based VoIP steganalysis. In CAST, we introduce a way to align spatial and temporal features through contrastive analysis. Experimental results demonstrate that this alignment is resource-efficient and could enhance detection accuracy. Meanwhile, CAST outperforms other state-of-the-art models in most scenarios.

With the growing volume of VoIP traffic, many steganography algorithms exploit VoIP speech as a carrier, posing a threat to cybersecurity. Among them, quantization index modulation (QIM)-based VoIP steganography has demonstrated excellent stealth, making detection difficult. In recent years, more studies have focused on developing feasible QIM-based VoIP steganalysis methods for detecting QIM-based VoIP steganography. Previous studies have mostly focused on improving detection performance while neglecting efficiency, resulting in insufficient research on lightweight models. In online detection scenarios, detection efficiency is crucial. On the one hand, the long inference time of large models can delay warnings. On the other hand, the high computational requirements of these models make them difficult to deploy on remote devices, which reduces their practical value. In this letter, we propose a simple yet efficient model named EQVS (efficient QIM-based VoIP steganalysis network) for detecting QIM-based VoIP steganography. In EQVS, the fold and unfold operations are redesigned based on the characteristics of VoIP speech samples and the requirements of the QIM-based VoIP steganalysis task, to avoid disrupting correlation features. Then, multi-codeword priority attention mechanism, inspired by the multi-query attention and retention mechanisms, redefines the calculation procedure for the query, key, and value matrices, as well as the normalization and softmax operations, to further reduce computational resource consumption in a single attention head. Experimental results demonstrate that EQVS outperforms other state-of-the-art models in both detection performance and efficiency.

Quantization index modulation (QIM) based VoIP steganography can conceal secret information in VoIP streams. Malicious users could use this technology to conduct illegal activities, threatening network and public security. Hence, practical steganalysis models that could detect QIM-based VoIP steganography are urged to be developed. In recent years, deep learning (DL) models have been investigated for this task, and exciting outcomes have been achieved. However, existing models are far from practical. Two major challenges are required to be addressed. First, there is still significant room for improvement in detection accuracy. Second, studies that balance the detection accuracy and response time are still insufficient. In this context, our main research topic fits in the QIM-based VoIP steganalysis theme, which aims to detect QIM-based steganography in VoIP streams in a fast and accurate manner.

Abstract Software fault localization is commonly recognized as arduous and time consuming. Spectrum‐based fault localization (SBFL) has been widely used due to its lightness. However, the effectiveness of SBFL is limited since it only considers simple statistics on the coverage information, ignoring the tie problem that the spectrum matrixes of some statements are the same. Most existing deep learning‐based fault localization (DLFL) techniques convert the coverage information into a vector, which utilizes the spectrum in a simplified manner and still has limitations in practice. To solve the above problem, we propose an approach via the weighted execution graph and graph attention network (WEGAT). We use a graph structure to represent the coverage information between test cases and program elements. Then, we generate a weighted execution graph by applying the predicate execution sequence. Furthermore, we combine the weighted execution graph with the AST as an integrated graph, which is the input of the GAT for fault localization. We evaluate WEGAT in within‐project and cross‐project prediction scenarios on the Defects4J benchmark. Experimental results show that our approach outperforms traditional SBFL (Ochiai, DStar and Tarantula) and DLFL (TraPT, CNN‐FL, Grace, and AGFL) methods, effectively improving the accuracy of fault localization.

In the past decades, there have been many studies on VoIP steganalysis models for quantization index modulation (QIM) based steganography. However, most of the proposed models in these studies did not consider resource consumption, limiting the application scenarios of these models. Inspired by MobileViT, we proposed a lightweight VoIP steganalysis model in this letter named LStegT (Lightweight steganalysis transformer), which could combine the strengths of convolutional neural networks (CNN) and transformers. First, LStegT utilizes 1D deep-wise separable convolutions to capture the correlations among codewords (local correlations). Then, LStegT applies a transformer encoder to encode the correlations among frames (global correlations). The application of deep-wise separable convolutions could significantly reduce the computation resource consumption. Besides, the transformer encoder in LStegT requires fewer training parameters because it only needs to encode the correlation among frames. In this letter, we exhibit how we designed the architecture of LStegT based on the characteristics of VoIP streams and QIM-based steganography. Also, we explained why LStegT is lightweight from the MobileViT perspective. Finally, our experimental results show that LStegT performs superbly in detecting QIM-based steganography in VoIP streams.

Software fault localization is notoriously tedious and time-consuming. Developed rapidly, machine learning techniques have been adopted for fault localization by researchers. Most existing approaches use the test coverage information as feature input to the learning model, ignoring the limited ability of the single-dimensional features. The effectiveness of fault localization is not greatly improved. To overcome the limitation, we propose a fault localization approach based on Bidirectional Recurrent Neural Networks (BiRNNs) and multi-dimensional features. Our approach collects suspiciousness-based, text similarity-based and fault-proneness-based features from the traditional fault localization areas and software metrics. To evaluate our approach, the experiments have been studied on the real-fault benchmark Defects4J and seeded fault program NanoXML. The experimental results show that our approach effectively improves fault localization accuracy.