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Paper ID:IJSREM62842

STEGOVISION: AI-BASED HIDDEN DATA TRANSMISSION USING MULTIMEDIA FILE

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62842

Volume:

10 |

5 |

May 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62842

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VS

Vadapalli Sathish

Computer Science and Engineering

Bharath Institute of Science and Technology (BIST), 173, Agaram Road, Selaiyur, Tambaram, Chennai- 600073, Tamil Nadu.

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TSK

T Sai Krishna

Computer Science and Engineering

Bharath Institute of Science and Technology (BIST), 173, Agaram Road, Selaiyur, Tambaram, Chennai- 600073, Tamil Nadu.

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PCKR

Puli Charan Kumar Reddy

Computer Science and Engineering

Bharath Institute of Science and Technology (BIST), 173, Agaram Road, Selaiyur, Tambaram, Chennai- 600073, Tamil Nadu.

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SPK

Seelam Pavan Kumar

Computer Science and Engineering

Bharath Institute of Science and Technology (BIST), 173, Agaram Road, Selaiyur, Tambaram, Chennai- 600073, Tamil Nadu.

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Mentors

DPT

Dr. P. Thamarai

Computer Science and Engineering

Bharath Institute of Science and Technology (BIST), 173, Agaram Road, Selaiyur, Tambaram, Chennai- 600073, Tamil Nadu.

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Abstract

As technology advances through the growth of digital communication networks, chances of cyberattacks, hacking, and data breach have increased beyond imagination. At present, while the necessity of encrypting data cannot be undermined, it becomes extremely important to conceal even the existence of the communication process. While cryptography could be used to secure the communication processes in question from the prying eyes of outsiders in the sense that all communication is encrypted, there could be chances of compromising the safety of the sender himself since his identity would be revealed in the entire process. Keeping in mind such a scenario, this study brings forth a framework that utilizes AI for multimedia steganography purposes. The secret messages in this case are concealed into images, videos, and audio files. Contrary to traditional ways of doing multimedia steganography, which evenly spreads the secret information across all sections of the media file, this method employs AI to identify media sections which have complicated textures or natural distortions or noise and then embeds the data inside the identified sections. In doing so, the method helps limit any further distortions of the media file as well as increase the complexity in statistical detection of the data embedded within the host file. Before the embedding process, the secret message is encoded into bits, which may be encrypted if necessary, to avoid any modifications of the information should anyone succeed in decrypting the data. On the other hand, the extraction process consists of extracting and decoding the embedded data from the host media file with minimal distortion of the host file. Various experimental investigations have indicated that this approach results in lower distortion rates, high hiding capabilities, reliable data decoding process as well as good audiovisual qualities of the generated output files. Therefore, this approach can be applied in applications like defence, business security, digital watermarking, and internet of things (IoT).

KEYWORDS

defence business security digital watermarking and internet of things (IoT).

Cite This Article

Vadapalli Sathish, T Sai Krishna, Puli Charan Kumar Reddy, Seelam Pavan Kumar, Dr. P. Thamarai, "STEGOVISION: AI-BASED HIDDEN DATA TRANSMISSION USING MULTIMEDIA FILE," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-6, May 2026. https://doi.org/10.55041/IJSREM62842

Full Article/Conclusion

In this paper, an AI-based scheme for secure communication through multimedia files by embedding hidden information has been introduced. The suggested scheme incorporates the fundamental ideas of steganography and AI-based intelligent region selection to provide secure and effective communication. The selection of appropriate regions like edges and textures is performed by utilizing AI, thereby ensuring minimal distortion of the images while keeping the risks of detection at a minimum. In contrast to the existing techniques of data embedding through multimedia, the incorporation of AI technology offers greater flexibility in terms of implementation and ensures better security. It has been observed through experimentation that the stego-media produced through the system is characterized by high quality, minimal distortion, and reliable data extraction at the receiver end. Moreover, the proposed algorithm is highly resistant to attacks related to general image processing and elementary steganalysis techniques. In summary, the proposed scheme meets all the necessary criteria including imperceptibility, capacity, security, and effectiveness.

References

1.   J. Liang et al., “A Steganography-Based Scheme for Secure Transmission of Monitoring Data in Smart Substations,” IEEE Internet of Things Journal, 2025. 2.      A. Aljarf, “DL-Steg: A Deep Learning-Based Image Steganography Model Using SAE and LSTM,” Springer Journal of Big Data, 2025. 3.    Y. Zhao et al., “Image Steganography Based on Wavelet Transform and Generative Adversarial Networks,” Journal of Visual Communication and Image Representation, 2025. 4.     S. Rahman et al., “A Novel and Efficient Digital Image Steganography Technique for Secure Communication,” Scientific Reports (Nature), 2025. 5.    B. Song et al., “A Survey on Deep Learning-Based Image Steganography,” Expert Systems with Applications, 2024. 6.   Y. Fernando et al., “Steganography on Image Metadata for Robust Data Hiding,” International Journal of Informatics Visualization, 2024. 7.       H. Ragab et al., “Digital Image Steganography and Reversible Data Hiding: A Review,” Journal of Image and Graphics, 2025 8.     F. A. Khalil et al., “A Review of Image Steganography Based on Metaheuristic Algorithms,” Computer Science Review, 2025. 9.   A. Krishnan et al., “Audio-in-Image Steganography Using Analysis and Resynthesis Sound Spectrograph,” IEEE Access, 2025. 10.    D. Darwis et al., “Metadata-Based Video Steganography Technique for Secure Data Communication,” Engineering, Technology & Applied Science Research, 2025.

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Publication Date

5/8/2026

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Double-Blind Peer Review

Review Rounds: 2

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