dcass

A zero-modification covert communication framework that encodes encrypted payloads into semantic selections of cover media (text, images, audio) using multi-modal vector search rather than modifying file bytes.

Traditional steganography alters the binary data of media files (such as LSB editing), creating statistical noise and byte-level anomalies that modern machine-learning-based steganalysis easily detects.

DCASS bypasses byte modification entirely. Instead of altering existing files, it maps secret payloads to semantically matching cover media (paragraphs of text, images, or audio clips) retrieved from a pre-indexed corpus. This is achieved using CLIP/CLAP multi-modal embeddings and a high-performance FAISS vector database. A GAN-based warden and a Reinforcement Learning agent run in an adversarial loop to continuously evaluate and optimize the selection's stealth.

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