A modular, extensible toolkit for hiding information across different data modalities—text, audio, and images—using both classical and deep-learning approaches.

This project evolves through three major phases:

1. Classical Steganography (LSB-based)
2. Deep Learning Steganography (CNN Encoder–Decoder)
3. Implicit Neural Representations (INRs)

The goal is to provide a unified playground for research, experimentation, and demonstration of cross-modal steganographic systems.

Traditional bit-level embedding methods act as the baseline for evaluating more advanced models.

• Hide text inside images (PNG/JPG recommended)
• Extract hidden messages losslessly
• Optional zlib compression for payload reduction
• Optional XOR-based encryption for basic confidentiality
• Bit-level packing/unpacking routines for fine-grained control

• Hide WAV audio inside images using LSB manipulation
• Extract embedded audio with low reconstruction error
• Sample-accurate modifications to keep audio artifacts minimal

Learned steganography using convolutional neural networks.

• Encoder embeds a secret image into a cover image
• Decoder reconstructs the hidden secret from the stego output
• Lightweight CNN architecture optimized for:
  • CPU-only machines
  • Optional CUDA acceleration when available
• PSNR-based quality analysis for:
  • Cover vs. Stego
  • Secret vs. Recovered Secret
• Designed to run on low-end laptops using small resolutions (e.g. 128×128)

Next-generation steganography using Implicit Neural Representations (INRs), encoding information directly into the weights of a continuous neural field rather than manipulating discrete pixel grids.

• Shared SIREN backbone with two output heads — one for the cover, one for the secret
• Secret is encoded into a key-shifted coordinate space — without the correct integer key, extraction produces noise
• Supports all combinations of: image, audio, text, and video as cover or secret
• Supports .png, .jpg, .wav, .mp3, .flac, .ogg, .aac, .m4a, .mp4, .avi, .txt
• Five quality presets: fast / low / medium / high / ultra — auto-selected based on hardware
• Adaptive loss weighting per modality — text and audio secrets receive higher training priority
• PSNR-based quality evaluation with per-modality verdicts in report.json (GOOD / OK / POOR / UNUSABLE)
• Each run saves to a unique timestamped subfolder — no output files are ever overwritten
• Live console warning if secret PSNR falls below usable threshold after training

This phase is compute-intensive. A GPU is strongly recommended for usable results:

• On CPU, the fast preset (~1 min) is available to verify the pipeline works, but output quality will likely be poor
• The minimum recommended preset for real results is low (~15–45 min on CPU depending on machine)
• Google Colab (free tier T4 GPU) is the recommended environment for students without a GPU — medium quality runs in ~3 minutes
• Upload inrCM.py + your input files to Colab and run:

!pip install torch librosa soundfile Pillow
!python inrCM.py --modal1 cover.png --modal2 secret.txt --mode hide --key 42 --quality medium

• Provide a unified framework for experimenting with multiple steganographic approaches
• Enable comparison between:
  • Handcrafted LSB methods
  • CNN-based deep learning methods
  • INR-based implicit representations (cross-modal, weight-space encoding)
• Serve as a reference implementation for coursework, research projects, and demos
• Achieve truly cross-modal steganography, where different data types (image, audio, text, video) can be embedded into each other via learned INR models

steganalysis.py is a unified, blind steganalysis tool covering all three project phases (LSB, CNN, INR). Given a cover and stego image, it computes imperceptibility metrics (PSNR, SSIM, MSE, SNR, UQI, NCC, BER), runs classical detection tests (Chi-square, RS analysis), analyses entropy, bit-planes, and frequency-domain noise, and produces a rule-based detection verdict.

Currently supports image inputs only (LSB and CNN outputs, plus image-modality INR outputs). Audio/video/text steganalysis for the INR module is planned as a separate extension.

python steganalysis.py --cover cover.png --stego stego.png
python steganalysis.py --cover cover.png --stego stego.png --mode inr --secret secret.png
python steganalysis.py --cover cover.png --stego stego.png --plot results --output report.json

Full pipeline breakdown: steganalysis_architecture.md