# Adaptive network steganography using deep learning and multimedia video analysis for enhanced security and fidelity

## TL;DR

This paper introduces a new method for secure video communication using deep learning to improve steganography in multimedia.

## Contribution

A deep learning-based adaptive network steganography framework for enhanced security and fidelity in multimedia videos.

## Key findings

- The proposed algorithm achieves a 95% steganographic success rate.
- It attains a PSNR of 48.3 dB, showing improved fidelity over existing methods.

## Abstract

This study presents an advanced adaptive network steganography paradigm that integrates deep learning methodologies with multimedia video analysis to enhance the universality and security of network steganography practices. The proposed approach utilizes a deep convolutional generative adversarial network-based architecture capable of fine-tuning steganographic parameters in response to the dynamic foreground, stable background, and spatio-temporal complexities of multimedia videos. Empirical evaluations using the MPII and UCF101 video repositories demonstrate that the proposed algorithm outperforms existing methods in terms of steganographic success and resilience. The framework achieves a 95% steganographic success rate and a peak signal-to-noise ratio (PSNR) of 48.3 dB, showing significant improvements in security and steganographic fidelity compared to contemporary techniques. These quantitative results underscore the potential of the approach for practical applications in secure multimedia communication, marking a step forward in the field of network steganography.