Khao2

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Every single bit.

Easy to use, informative and comprehensive steganalysis suite.

This is a CLI tool for my SaaS sidehustle (khao2.com) for steganalysis.

FAQs

But what really is Khao2/K2?

K2 is a versatile steganography detection platform designed for steganalysis on images.

Why is it different to something like Stegoveritas?

K2 is different because it uses a broad spectrum of statistical and mathematical tests that check for small anomalies that highly sophisticated algorithms (e.g. J-UNIWARD, JMiPOD) leave behind, instead of looking for direct signatures it looks for anomalies/signals.

What is the ML part for?

The ML part of K2 is used for aggregating all anomalies and outputting a binary verdict on how likely said image is to contain steganography.

Installation

pip install -e .

Usage

Get a token:

Sign up at app.khao2.com
Create an API key in the API Keys tab

Configure your API token:

k2 token set k2_yourtokenhere32characterslower

Configure API endpoint:

k2 endpoint set https://api.khao2.com

Basic Commands

Analyze an image:

k2 dig image.png

Watch scan progress in real-time:

k2 dig image.png --watch

Get scan results by ID:

k2 get <scan-id>

Batch Processing

Process multiple images:

k2 batch image1.png image2.jpg image3.gif

Recursively scan directories:

k2 batch /path/to/images --recursive

Custom file patterns:

k2 batch /path/to/files --pattern "*.png" --recursive

Plugin Management

List available plugins:

k2 plugins list

Load a plugin:

k2 plugins load batch_processor

Reporting

Generate HTML dashboard report:

k2 report dashboard.html

Generate executive summary (last 7 days):

k2 report executive_summary.html --executive --days 7

Export as PDF:

k2 report analysis.pdf --format pdf

Advanced Features

List recent scans:

k2 list --limit 10

Check quota:

k2 quota

View usage analytics:

k2 usage

Plugin Architecture

Khao2 features a powerful plugin system that enables extensibility and advanced functionality:

Built-in Plugins

Batch Processor: Intelligent batch processing with concurrency control and smart routing
Enterprise Collaboration: Team workspaces, workflow automation, and audit trails
Hybrid Processing: Cloud-local analysis with caching and cost optimization
Advanced Intelligence: Pattern recognition and continuous learning from scan results
Reporting & Visualization: Comprehensive dashboards and export capabilities

Plugin Types

Detector Plugins: Custom steganography detection algorithms
Processor Plugins: Batch processing and automation workflows
Exporter Plugins: Custom report formats and integrations
Analyzer Plugins: Advanced analysis and intelligence features
Integration Plugins: Third-party service integrations

Developing Custom Plugins

Create a plugin by implementing the appropriate protocol and placing it in the plugins directory. See the built-in plugins for examples.

Info

This is a personal project turned SaaS side hustle, I originally built this for my own CTF challenges with no intention of making it a product.

Pricing

People may say 5 scans a month for free is too little, but please bear in mind that these statistical scans are sequential by nature and take upwards of + minutes where they run on Modal.com HPC.

Cool stuff

It caught J-UNIWARD at 0.2 BPP
Very good at catching LSB
Uses ML to classify all statistical tests for a human readable view

Example CLI output:

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KHAO2 IMAGE FORENSICS | Every little bit.

✅ ANALYSIS COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

VERDICT: BENIGN
Possibility: 8%
Confidence: 85.0%

File: test.png
Size: 88 bytes | 1x1 | PNG

SSDEEP: 3:yionv//thPlE+tnMysyxdk/Slc+0kxQcnd6vtjp:6v/lhPfZMys+k/WT0krnd6vdp
SHA512: cd96b0688ad40f6b8ea0f2dd5529ae14d0037fbf8b30e1a1d0fd1a81a16e4c4305c95f1673aee7808a618c70ce4d0d4706c31dc7fb8cf4deab39c8ce584d7535
SHA256: 39768f51d067905ee91c1422fe26ea2cc978ff0ac12bb61b3878f094c2cd1db3
MD5: b34c91ec15a592d974131f02c1f05cb6

ENGINES: 329/339 completed | 10 failed
Runtime: 373489ms | 29.84K FLOPs

FILE INTEGRITY
├─ Format: PNG image, 1 x 1, 8-bit/color RGBA, non-interlaced
├─ Mode: RGBA

STATISTICAL ANALYSIS
├─ Entropy: -0.0
├─ Size Score: 0.09
└─ Strings Found: 0

ANOMALIES DETECTED: 1 (85.0% CONFIDENCE)
! #082_CLA Perfect lag correlation in a 1x1 image, likely trivial and not indicative of hidden data
  Confidence: 50.0% | Anomaly Value: 10.0

IDENTIFIED MEDIUM: none
IDENTIFIED VECTOR: No hidden data detected; image size and entropy too low for embedding
TECHNIQUE CARDINALS: LSB Replacement,PNG ancillary chunks,Palette LSB

RECOMMENDED NEXT STEPS:
  Run pngcheck and exiftool to list all PNG chunks, use StegDetect or zsteg on the file, and re‑run binwalk with deeper recursion to confirm no hidden payloads.

POTENTIAL FALSE NEGATIVE REASONS:
  • Extremely small carrier (30% likelihood)
  • Stego in unparsed metadata (25% likelihood)
  • Custom proprietary embedding (20% likelihood)
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Development

Run tests:

pytest

Install in development mode:

pip install -e .

odinglyn0/K2-CLI