International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Robust Video Data Hiding Using Steganography

Author(s) Prof. Santosh M, Danish Mushtaq, Kamil Hussain Baig
Country India
Abstract In today’s digital era, the exponential growth of multimedia data has driven a growing demand for secure communication and data protection. Among the various methods to ensure privacy and confidentiality, steganography involves concealing information within a digital medium, such as images, audio, or video, in a way that it remains imperceptible to unauthorized observers. Video steganography specifically leverages video files as carriers, or "cover media," to embed hidden information. Videos are ideal for such purposes due to their large data capacity and inherent redundancies across frames. Steganography is a crucial technique for secure data communication, enabling the concealment of sensitive information within digital media. This project presents a robust video data-hiding scheme based on steganography, implemented using TensorFlow. The proposed method leverages deep learning techniques to optimize the embedding and extraction of hidden data, ensuring resilience against common distortions such as compression, noise, and frame loss. By utilizing spatial and temporal redundancies in video content, the system achieves high payload capacity, minimal visual degradation, and strong robustness. Experimental results demonstrate the effectiveness of the method, making it suitable for secure and reliable data transmission in real-world scenarios
Keywords Video steganography, data hiding, deep learning, TensorFlow, secure communication, multimedia security, spatial and temporal redundancy, payload capacity, robustness
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-16
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.44874
Short DOI https://doi.org/g9kfvv
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E-ISSN 2582-2160
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