DCASS

Dynamic Context-Aware Semantic Steganography via Zero-Modification Media Curation

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📌 Overview

DCASS is a research-oriented system for semantic steganography that enables covert communication without modifying any carrier media.
Instead of embedding data into pixels, audio samples, or bitstreams, DCASS encodes messages by curating semantically aligned, naturally occurring media (text, images, audio) and distributing them using human-like behavioral patterns.

This project explores the intersection of:

• Semantic communication
• Multi-modal embeddings
• AI-driven stealth (GANs & Reinforcement Learning)
• Traffic analysis evasion

The system is designed as a proof-of-concept prototype accompanied by a research paper.

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🎯 Key Idea

If a message can be represented by meaning rather than bits, then existing content can act as a carrier without ever being altered.

DCASS:

• Encodes messages into semantic vector sequences
• Retrieves existing media from a large corpus
• Uses dynamic context keys to prevent static mappings
• Distributes content using behaviorally realistic schedules
• Achieves stealth against both content-based steganalysis and traffic analysis

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✨ Core Features

• Zero-Modification Steganography
  No changes to carrier media → resistant to classical steganalysis

• Multi-Modal Support
  Text, image, and audio-based semantic encoding

• Unified Vector Search
  FAISS-based high-performance similarity search

• Dynamic Context Awareness
  Time, public data, and contextual keys affect encoding

• AI-Based Stealth

  • GAN-based human behavior scheduler
  • Reinforcement Learning agent for adaptive stealth

• Adversarial Evaluation Traffic analysis, stealth metrics, and benchmarking

• CLI-Based Prototype Fully controllable via command line

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🧠 System Architecture

DCASS is organized into four logical layers:

1. Corpus & Indexing

   • Large-scale text, image, and audio datasets
   • Semantic embeddings (Sentence-Transformers, CLIP, CLAP)
   • Unified FAISS vector index

2. Encoding / Decoding Engine

   • Semantic chunking
   • Message ↔ vector sequence transformation
   • Dynamic context key derivation
   • Error correction mechanisms

3. Stealth & Distribution

   • GAN-based behavioral scheduler
   • RL-based policy agent
   • Multi-channel content distribution

4. Analysis & Testing

   • Performance benchmarks
   • Adversarial traffic analysis
   • Stealth and accuracy metrics

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🧰 Technology Stack

Component	Technology
Language	Python
ML Framework	PyTorch
Embeddings	Sentence-Transformers, CLIP, CLAP
Vector DB	FAISS
GAN	Custom PyTorch implementation
RL	Stable-Baselines3 / RLlib
CLI	Typer / Click
Data Processing	NumPy, Pandas, Librosa

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📁 Project Structure

dcass/
├── src/
│   ├── corpus/          # Dataset loading, preprocessing, embeddings, FAISS
│   ├── engine/          # Encoding / decoding logic
│   ├── stealth/         # GAN scheduler and RL agent
│   ├── distribution/   # Multi-channel dispatcher
│   ├── analysis/        # Benchmarks and adversarial testing
│   └── cli/             # Command-line interface
│
├── data/
│   ├── raw/
│   ├── processed/
│   └── embeddings/
│
├── models/              # Trained GAN and RL models
├── tests/               # Unit, integration, adversarial tests
├── notebooks/           # Research experiments
├── docs/                # Architecture & research paper
└── scripts/             # Utility scripts