# Exploring AI in Steganography and Steganalysis: Trends, Clusters, and Sustainable Development Potential

arXiv: https://arxiv.org/abs/2511.12052 · HTML: https://mlsanity.com/paper/2511.12052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 113 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Digital Watermarking for Deep Neural Networks](https://mlsanity.com/paper/1802.02601/) (2018)
- [CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography](https://mlsanity.com/paper/1803.09043/) (2018)
- [Invisible Steganography via Generative Adversarial Networks](https://mlsanity.com/paper/1807.08571/) (2018)
- [TS-RNN: Text Steganalysis Based on Recurrent Neural Networks](https://mlsanity.com/paper/1905.13087/) (2019)
- [StegNet: Mega Image Steganography Capacity with Deep Convolutional Network](https://mlsanity.com/paper/1806.06357/) (2018)
- [The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis](https://mlsanity.com/paper/2011.00223/) (2020)
- [Three-Dimensional Mesh Steganography and Steganalysis: A Review](https://mlsanity.com/paper/2104.10203/) (2021)
- [Large-scale JPEG steganalysis using hybrid deep-learning framework](https://mlsanity.com/paper/1611.03233/) (2016)
- [Detection of Malicious and Low Throughput Data Exfiltration Over the DNS Protocol](https://mlsanity.com/paper/1709.08395/) (2017)
- [Information Hiding as a Challenge for Malware Detection](https://mlsanity.com/paper/1504.04867/) (2015)

## Abstract (arXiv)

Steganography and steganalysis are strongly related subjects of information security. Over the past decade, many powerful and efficient artificial intelligence (AI) - driven techniques have been designed and presented during research into steganography as well as steganalysis. This study presents a scientometric analysis of AI-driven steganography-based data hiding techniques using a thematic modelling approach. A total of 654 articles within the time span of 2017 to 2023 have been considered. Experimental evaluation of the study reveals that 69% of published articles are from Asian countries. The China is on top (TP:312), followed by India (TP-114). The study mainly identifies seven thematic clusters: steganographic image data hiding, deep image steganalysis, neural watermark robustness, linguistic steganography models, speech steganalysis algorithms, covert communication networks, and video steganography techniques. The proposed study also assesses the scope of AI-steganography under the purview of sustainable development goals (SDGs) to present the interdisciplinary reciprocity between them. It has been observed that only 18 of the 654 articles are aligned with one of the SDGs, which shows that limited studies conducted in alignment with SDG goals. SDG9 which is Industry, Innovation, and Infrastructure is leading among 18 SDGs mapped articles. To the top of our insight, this study is the unique one to present a scientometric study on AI-driven steganography-based data hiding techniques. In the context of descriptive statistics, the study breaks down the underlying causes of observed trends, including the influence of DL developments, trends in East Asia and maturity of foundational methods. The work also stresses upon the critical gaps in societal alignment, particularly the SDGs, ultimately working on unveiling the field's global impact on AI security challenges.