MAster assignment
Context-Aware Steganography using Ontology
Type: Master CS or Master BIT
Period: Start date: as soon as possible
Student: Unassigned
If you are interested please contact:
Objective:
Traditional steganography techniques often overlook the meaning or context of the cover media, making hidden data easier to detect through statistical or structural analysis. This project proposes a context-aware steganographic system that uses ontologies to understand the semantic content of images or text. By assigning importance scores to image regions or text segments and reasoning about their suitability for embedding, the system can hide information more intelligently and subtly. The approach aims to improve resistance to detection, maintain media quality, and adapt dynamically to different content types, to name a few, such as natural scenes, structured scenes (buildings), human subjects (faces). A semantic-aware steganalysis tool will also be developed to test the robustness of the proposed method.
Requirements:
- Python programming (image and text processing, machine learning)
- Basic knowledge of steganography (e.g., LSB, text-based techniques)
- Familiarity with ontologies
References
- Semantic Steganography: A Framework for Robust and High-Capacity Information Hiding using Large Language Models, https://arxiv.org/abs/2412.11043
- Ontology-based Secure Retrieval of Semantically Significant Visual Contents, https://arxiv.org/abs/1510.02177