Comparative Analysis of Steganography Injection and Detection Tools/Methods: Traditional vs. AI-Based
Document Type
Conference Proceeding
Source of Publication
Proceedings of the International Symposium on Digital Forensics and Security Isdfs
Publication Date
3-19-2026
Abstract
The paper presents a cross-media benchmarking study of steganography injection and steganalysis tools for images, audio, and video under a unified protocol. Traditional and learning-based methods are compared using shared datasets, fixed payload settings, and standardized metrics, and a weighted scoring rubric is introduced to summarize imperceptibility, robustness, security, computational cost, and detection behaviour. Results show that highly imperceptible image embedding (e.g., LSB-based tools with ≈ 0.01% pixel modification) can still be detected reliably, while aggressive embedding (e.g., F5 with 98.78% pixel modification) produces visible artifacts and remains detectable. For audio and video, metadata, and structure-based indicators enable reliable detection, with the combined audio detection strategy achieving 100% identification of stego files while preserving clean-file accuracy. These results support evidence-based tool selection for digital forensics triage and secure deployment under operational constraints.
DOI Link
ISBN
[9798331573102]
ISSN
Publisher
IEEE
Issue
2026
Disciplines
Computer Sciences
Keywords
Chi-Square Test, Deep Learning, Digital Forensics, Discrete Cosine Transform (DCT), Least Significant Bit (LSB), Machine Learning, RS Analysis, Steganalysis, Steganography
Scopus ID
Recommended Citation
Alnuaimi, Rahaf; Almarzooqi, Maryam; and Iqbal, Farkhund, "Comparative Analysis of Steganography Injection and Detection Tools/Methods: Traditional vs. AI-Based" (2026). All Works. 8202.
https://zuscholars.zu.ac.ae/works/8202
Indexed in Scopus
yes
Open Access
no