A deep learning-driven multi-layered steganographic approach for enhanced data security
Abstract In the digital era, ensuring data integrity, authenticity, and confidentiality is critical amid growing interconnectivity and evolving security threats. This paper addresses key limitations of traditional steganographic methods, such as limited payload capacity, susceptibility to detection,...
Saved in:
Main Authors: | Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mosleh Abualhaj, Emran Alzubi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-89189-5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Evaluation of LSB Steganography on Image File Using 3DES and MD5 Key
by: Ilham Firman Ashari, et al.
Published: (2024-03-01) -
Separation ratios of maps between Banach spaces
by: Rosendal, Christian
Published: (2023-11-01) -
Positional embeddings and zero-shot learning using BERT for molecular-property prediction
by: Medard Edmund Mswahili, et al.
Published: (2025-02-01) -
An Enhanced Text Steganography Technique (SHA-Logic): Based on Conditional Logic and SHA-3 (Secure Hash Algorithm 3)
by: Kisakye Diana Michelle, et al.
Published: (2024-12-01) -
On the small scale nonlinear theory of operator spaces
by: Braga, Bruno M., et al.
Published: (2024-12-01)