In recent years, research on information-hiding techniques based on network streaming media has focused on how to covertly embed secret information within real-time transmissions to achieve clandestine communication. The misuse of such technologies poses significant security risks, such as the dissemination of malicious codes, commands, viruses, and more. The existing methods for steganalysis of network voice streams generally face challenges in universality, exhibiting poor adaptability to steganographic detection scenarios with non-identity distributions. To address these issues, we introduce a framework named the Domain-Aware Enhanced Framework for VoIP Steganalysis (DAEF-VS), which harnesses the CutMix technology to enhance the shared steganographic domain features and employs the Domain-Aware Learning Model to fine-tune these features, thereby significantly improving generalization capabilities. Extensive experimental results demonstrate that our approach vastly surpasses existing advanced methods in terms of universality across a variety of steganographic detection scenarios.

In recent years, there has been an increasing number of information hiding techniques based on network streaming media, focusing on how to covertly and efficiently embed secret information into real-time transmitted network media signals to achieve concealed communication. The misuse of these techniques can lead to significant security risks, such as the spread of malicious code, commands, and viruses. Current steganalysis methods for network voice streams face two major challenges: efficient detection under low embedding rates and short duration conditions. These challenges arise because, with low embedding rates (e.g., as low as 10%) and short transmission durations (e.g., only 0.1s), detection models struggle to acquire sufficiently rich sample features, making effective steganalysis difficult. To address these challenges, this paper introduces a Dual-View VoIP Steganalysis Framework (DVSF). The framework first randomly obfuscates parts of the native steganographic descriptors in VoIP stream segments, making the steganographic features of hard-to-detect samples more pronounced and easier to learn. It then captures fine-grained local features related to steganography, building on the global features of VoIP. Specially constructed VoIP segment triplets further adjust the feature distances within the model. Ultimately, this method effectively address the detection difficulty in VoIP. Extensive experiments demonstrate that our method significantly improves the accuracy of streaming voice steganalysis in these challenging detection scenarios, surpassing existing state-of-the-art methods and offering superior near-real-time performance.

Reverse time migration (RTM) is an important technology for imaging ground penetrating radar (GPR) data. To address the problem of artifacts flooding of imaging results and high memory consumption of RTM, we propose an optimized multitraces cross correlation window (MCW) to increase the order of magnitude difference between the signals and artifacts for more obvious separation effect, but it also exacerbates the problem of computational cost. With the high sampling rate and high efficiency of collection method, common offset GPR is convenient to acquire large amounts of data, which consumes more numerous cost for RTM. Due to the attenuation property of high-frequency radar waves, most of the signals of common offset GPR originate from a small region below the antenna. Inspired by the footprint in airborne electromagnetic method, we propose the finite domain (FD) strategy, which limits the calculation of single trace to FD, and combine it with optimized MCW. It can reduce the computational cost of RTM and MCW significantly at the same time, especially for long profile data. Numerical experiments show that the FD reduces the computation by 77.22% with speedup 11.01. The optimized MCW retains the effective information separated from artifacts. The migration of the measured data proves the advantages and practicality of this method in engineering practical exploration.

With the rapid advancements in gene editing and DNA synthesis technologies, DNA has emerged as a next-generation physical steganographic medium due to its high information capacity, robustness, and superior concealment capabilities. While this steganography can be used to protect information security, it also poses a risk of being exploited for illicit transmission of harmful information. Consequently, it is imperative to discern steganographic DNA sequences from a vast array of DNA sequences. To address this challenge, this letter introduces a high-performance DNA steganalysis framework named DS-CLF. Specifically, the DS-CLF framework leverages a Transformer encoder to extract and learn DNA features within a supervised contrastive learning framework using ingeniously constructed DNA sequence triplets. Extensive experimental results demonstrate that the DS-CLF framework is highly effective in capturing DNA sequence features, and its detection capabilities for the latest DNA steganography techniques significantly outperform the best methods to date.