INTELLIGENT METHODS OF VIDEO STREAM ANALYSIS FOR DETECTING HIDDEN MESSAGES

Abstract

The relevance of this study is determined by the rapid growth of video data volumes and the increasing sophistication of steganographic methods, which pose significant risks to information security and undermine the effectiveness of traditional monitoring tools. Conventional digital signal processing algorithms fail to provide sufficient accuracy and robustness in detecting hidden messages, particularly in the presence of noise interference and dynamically changing data streams. This necessitates the implementation of intelligent approaches that integrate methods of computer vision, machine learning, and multimodal analysis.The purpose of the article is to substantiate and develop intelligent methods of video stream analysis for detecting hidden messages, thereby enhancing the protection of the information environment and improving the efficiency of modern artificial intelligence algorithms under growing cyber threats.The research methodology is based on the integration of system analysis of video streams, machine learning techniques, deep neural networks, and multimodal data processing approaches. It applies principles of combining computer vision algorithms with classification models, including convolutional and recurrent networks, as well as transformer-based attention mechanisms. A comparative analysis of the effectiveness of different architectures was carried out, along with real-time system modeling and assessment of their resistance to manipulations.The results demonstrate the applicability of both traditional and intelligent methods for identifying hidden signals.The study confirms that deep learning and multimodal systems provide significantly higher accuracy and adaptability compared to traditional approaches. It was established that integrated architectures analyze spatial, temporal, and contextual video characteristics, creating prerequisites for improving the detection of hidden messages in cybersecurity, border control, and critical infrastructure monitoring.The conclusions confirm that the application of intelligent video stream analysis methods overcomes the limitations of traditional solutions and significantly strengthens information security. At the same time, key implementation challenges were identified, including high computational costs, a shortage of annotated multimodal data, low interpretability of results, and vulnerability to adversarial attacks.Future research prospects are linked to the development of adaptive architectures based on continual learning and federated learning, model optimization for energy-efficient functioning, the introduction of explainable AI to increase algorithm transparency, and the creation of unified protocols for integration with other cybersecurity tools. These advancements are expected to shape a new generation of intelligent video analysis systems capable of effectively responding to current and future information threats.