Journal of Cybersecurity and Information Management

The invention of deepfake applications make it possible to produce highly natural and real voice recordings which creates critical concerns about the credibility of audio telecommunications. The confirmation of the speakers’ voices became essential especially for sensitive data such as financial, healthcare, and surveillance risk management services, authentication of speakers’ voices became significantly crucial. To improve solutions to this issue, this paper presents MACSteg strategy which is a real-time, lightweight voice authentication technique by discreetly encapsulate device’s MAC address within voice file using Quantization Index Modulation (QIM) stego-technique. Unlike many traditional strategies that degrade voice quality or produces noticed jitter, MACSteg technique preserve both clarity and efficiency. Implementations showed that the hidden MAC address stayed intact in spite of some typical voice processing such as compression, while interfered signals reformed by clatter or volume variations were consistently detected. The proposed system obtained a high signal-to-noise ratio (SNR) exceeding 70 dB, illustrating that the alterations were inaudible, and maintained well in real-time submissions, giving only a processing delay of 0.01 milliseconds per each audio segment. The results indicate MACSteg’s potential as a ascendable and effective approach for safeguarding voice authenticity, especially in circumstances where verification of speaker’s voice is vital.

Face recognition in unconstrained environments is difficult due to varying poses and lighting conditions. This can severely impair the performance of intelligent recognition models. Traditional methods often do not adapt well to these variations, which results in poor performance and limited applicability. This paper proposes a hybrid intelligent face recognition model based on hierarchical feature extraction and illumination normalization (H-FR). The proposed method employs a hierarchical feature extraction model to capture macro and micro facial details, ensuring reliable recognition across diverse poses and lighting conditions. Employing Adaptive Histogram Equalization on the A and B channels of the LAB colour space effectively normalizes illumination variations, enhancing the visibility and consistency of facial features. The proposed model has been tested and validated on the "Pins Face Recognition" dataset available on Kaggle, which encompasses various celebrity faces captured in varying poses and lighting conditions. The proposed model has been demonstrated through extensive experimentation to outperform AlexNet and VGG-19. The compared algorithms achieved accuracies of 88% for AlexNet and 93% for VGG-19, while the proposed H-FR model achieved 96%.