AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel cross-domain steganography framework for hiding images within videos, namely VSHI-Net is proposed, and a Noise-Based Invertible Neural Networks backbone is designed to improve resistance against perturbations during lossy transmission.
A comprehensive review of spatial domain image steganography by examining its historical development, fundamental concepts, classification, and major techniques, including Least Significant Bit (LSB), Adaptive LSB, Pixel Value Differencing (PVD), Pixel Indicator Technique (PIT), Optimal Pixel Adjustment Process (OPAP),...
Results reveal that the adaptive DWT design balances security, embedding efficiency, and visual quality, making it suitable for secure multimedia transmission and digital content protection.
Image steganography plays a crucial role in covert communication and copyright protection, but existing methods still struggle to achieve a balance between embedding capacity, image imperceptibility, anti-detection capabilities, and model complexity. Under complex channel interference such as compression and noise, the...
A review of the existing techniques into spatial domain-based, transform domain-based, hybrid and adaptive intelligent techniques, which highlights the current trends and outlines the future direction to develop next-generation secure image steganographic systems.
The suggested framework combines the utilization of adaptive embedding plans in various carrier media with the Discrete Wavelet Transform being employed in image stealth, the Discrete Cosine Transform being employed in audio embedding and AES-based encryption being employed in protecting the textual payload.
RIS-MoE is a robust and secure latent-space image steganography framework that integrates distortion-tolerant message representation with receiver-side adaptive latent restoration and consistently improves representative latent-space steganography pipelines as a plug-and-play restoration component, demonstrating its tr...
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