Due to the exponential increase in cyber/online crimes involving the use of steganography in streaming media, it is crucial to focus on studying steganalysis technology for the detection of covert communications. Several existing steganalysis methods rely on classifiers such as Support Vector Machines (SVM) and conventional statistical analysis. However, these methods suffer from high computational intensity and time consumption, making them unsuitable for real-time detection of steganography in streaming media. Additionally, some steganalysis techniques are specifically designed for certain codecs or depend on particular steganographic algorithms, limiting their practicality and universality. To address these challenges, this study devises five steganalysis algorithms tailored for Voice over Internet Protocol (VoIP) to detect covert channels in streaming media communications. The algorithms are as follows: ANOVA, derivative-based Mel Frequency Cepstrum Coefficient (DMFCC), improved Regular and Singular (RS) test, Mann–Whitney–Wilcoxon (M-W-W) test, and FFT-based Steganalysis. Experimental evaluations were performed on two sets of media streams — one without steganography and the other with steganography. The results obtained from these experiments reveal that the proposed VoIP steganalysis algorithms exhibit variations in terms of detection sensitivity, whether blind or targeted, online or offline, as well as the requirement for original samples.

Abstract Deep learning techniques have shown promise in audio steganalysis, which involves detecting the presence of hidden information (steganography) in audio files. However, deep learning models are prone to overfitting, particularly when there is limited data or when the model architecture is too complex relative to the available data for VoIP steganography. To address these issues, new deep‐learning approaches need to be explored. In this study, a new convolutional neural network for audio steganalysis, incorporating a multi‐scale feature fusion method and an attention mechanism, was devised to enhance the detection of steganographic content in audio signals encoded with G729a. To improve the network's adaptability, a multi‐scale parallel multi‐branch architecture was employed, allowing characteristic signals to be sampled with varying granularities and adjusting the receptive field effectively. The attention mechanism enables weight learning on the feature information after multi‐scale processing, capturing the most relevant information for steganalysis. By combining multiple feature representations using a weighted combination, the deep learning model's performance was significantly enhanced. Through rigorous experimentation, an impressive accuracy rate of 94.55% was achieved in detecting malicious steganography. This outcome demonstrates the efficacy of the proposed neural network, leveraging both the multi‐scale feature fusion method and the attention mechanism.

Abstract In the era of Big Data, the proliferation of multi-source online information has made false information control a crucial element in sustaining a healthy digital ecosystem. The significant societal harm caused by misinformation has spurred academic interest in developing robust authenticity detection methods for online content. To date, three primary paradigms have emerged for authenticity detection: unimodal, multimodal, and external knowledge-based approaches. This work provides a detailed investigation into existing false information detection techniques, selecting representative studies to review the current research landscape. Furthermore, it organizes commonly used datasets and evaluation metrics in the field and identifies promising directions for future research in false information detection.

Connected and automated vehicle technology via vehicle-to-everything communication, can assist in improving energy efficiency for hybrid electric vehicles (HEVs). In particular, information about the timing of traffic lights and surrounding vehicles can be exchanged between traffic vehicles and in conjunction with vehicle state information, to improve the fuel economy of HEVs significantly. To this end, we propose a multi-lane hierarchical optimization (MLHO) algorithm based on a predictive control framework. The dynamic behaviors of the surrounding vehicles are first predicted, and then the traffic light information (e.g., signal phasing and timing) and vehicles’ state information are utilized in the design. MLHO is a two-level strategy wherein a multi-lane speed planning method for a host vehicle is formulated to plan the optimal speed and lane-change behaviors by considering vehicle power demand, driving comfort, and safety in the upper level. In the lower level, dynamic programming is adopted to devise energy management by tracking the optimal speed. Simulation results under real routes using the traffic simulation software Simulation of Urban Mobility show that the fuel economy of MLHO is improved by 32% on average compared to speed profile driven by a human driver model. In addition, traffic efficiency is enhanced significantly, i.e., different traffic occupancy results on the road indicate that the proposed MLHO is less affected by the traffic flow density. With different traffic densities, the maximum fuel consumption difference under the three considered scenarios is only 0.645L/100km.

Voice over IP (VoIP) is finding its way into several applications, but its security concerns still remain. This paper shows how a new self-adaptive steganographic method can ensure the security of covert VoIP communications over the Internet. In this study an Active Voice Period Detection algorithm is devised for PCM codec to detect whether a VoIP packet carries active or inactive voice data, and the data embedding location in a VoIP stream is chosen randomly according to random sequences generated from a logistic chaotic map. The initial parameters of the chaotic map and the selection of where to embed the message are negotiated between the communicating parties. Steganography experiments on active and inactive voice periods were carried out using a VoIP communications system. Performance evaluation and security analysis indicates that the proposed VoIP steganographic scheme can withstand statistical detection, and achieve secure real-time covert communications with high speech quality and negligible signal distortion.