title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	license
2PAC Picture Analyzer & Corruption Killer	🔫	purple	blue	gradio	4.44.0	app.py	false	mit

🔫 2PAC: Picture Analyzer & Corruption Killer

Advanced image security and steganography toolkit

Features

🔒 Hide Secret Data

Invisibly hide text messages inside images using LSB (Least Significant Bit) steganography:

• Hide text of any length (capacity depends on image size)
• Optional password encryption for added security
• Adjustable LSB depth (1-4 bits per channel)
• PNG output preserves hidden data perfectly

🔍 Detect & Extract Hidden Data

Advanced steganography detection using RAT Finder technology:

• ELA (Error Level Analysis) - Highlights compression artifacts
• LSB Analysis - Detects randomness in least significant bits
• Histogram Analysis - Finds statistical anomalies
• Metadata Inspection - Checks EXIF data for suspicious tools
• Extract Data - Recover messages hidden with this tool

🛡️ Check Image Integrity

Comprehensive image validation and corruption detection:

• File format validation (JPEG, PNG, GIF, TIFF, BMP, WebP, HEIC)
• Header integrity checks
• Data completeness verification
• Visual corruption detection (black/gray regions)
• Structure validation

How It Works

LSB Steganography

The tool hides data in the least significant bits of pixel values. Since changing the last 1-2 bits of a pixel value (e.g., changing 200 to 201) is imperceptible to the human eye, we can encode arbitrary data without visible changes to the image.

Example:

• Original pixel: RGB(156, 89, 201) = 10011100, 01011001, 11001001
• After hiding bit '1': RGB(156, 89, 201) = 10011100, 01011001, 11001001 (last bit already 1)
• After hiding bit '0': RGB(156, 88, 201) = 10011100, 01011000, 11001001 (89→88)

This allows hiding hundreds to thousands of bytes in a typical photo!

Steganography Detection

The RAT Finder uses multiple forensic techniques:

1. ELA (Error Level Analysis): Re-saves the image at a known quality and compares compression artifacts. Hidden data or manipulation shows as bright areas.

2. LSB Analysis: Statistical tests check if the least significant bits are too random (hidden data) or too uniform (natural image).

3. Histogram Analysis: Analyzes color distribution for anomalies typical of steganography.

4. Metadata Forensics: Checks EXIF data for steganography tools or suspicious editing history.

Usage Tips

For Hiding Data:

• ✅ Use PNG images (JPEG compression destroys hidden data)
• ✅ Larger images = more capacity
• ✅ Use 1-2 bits per channel for undetectable hiding
• ✅ Add password encryption for sensitive data
• ⚠️ Don't re-save or edit the output image!

For Detection:

• 🔍 Higher sensitivity = more thorough but more false positives
• 📊 Check the ELA image for bright spots (potential hiding)
• 💡 High confidence doesn't guarantee hidden data (could be compression artifacts)
• 🔓 Use "Extract Data" tab if you suspect LSB steganography

For Corruption Checking:

• 🛡️ Enable visual corruption check for damaged photos
• ⚙️ Higher sensitivity for stricter validation
• 📁 Useful before archiving important photo collections

About

2PAC combines three powerful tools:

• LSB Steganography engine (new!)
• RAT Finder - Advanced steg detection
• Image Validator - Corruption checker

Created by Richard Young | Part of DeepNeuro.AI

🔗 GitHub Repository: github.com/ricyoung/2pac 🌐 More Tools: demo.deepneuro.ai

Security & Privacy

• ✅ All processing happens in your browser session (Hugging Face Space)
• ✅ Images are not stored or logged
• ✅ Temporary files are deleted after processing
• ✅ Your hidden data and passwords are never saved

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"All Eyez On Your Images" 👁️