Nightshade Antidote

Overview

Nightshade Antidote is an image forensics tool used to analyze digital images for signs of manipulation or forgery. It implements several common techniques used in image forensics including:

• Metadata analysis
• Copy-move forgery detection
• Frequency domain analysis
• JPEG compression artifacts analysis

The tool takes an input image, performs analysis using the above techniques, and outputs a report summarizing the findings.

Requirements

Nightshade Antidote requires the following Python packages:

• OpenCV
• Numpy
• Matplotlib
• Scipy
• PIL
• Collections
• Scikit-learn
• Exiftool

Usage

To use Nightshade Antidote, simply run the Python script on an input image:

python nightshade_antidote.py input.jpg

This will perform forensics analysis on input.jpg and output the results to the console and generate plots where relevant.

The script contains several functions that can be called independently to perform specific analyses:

• detect_copy_move - Detect copy-move forgery
• analyze_metadata - Extract and print metadata
• spectral_analysis - Frequency domain analysis
• pixel_ordering_check - Check DCT coefficients
• compression_artifacts_check - Check for JPEG artifacts
• file_format_check - Verify file format
• output_report - Generate analysis report

Output

Nightshade Antidote will output a comprehensive analysis report for the input image including:

• Metadata summary
• Copy-move forgery detection results
• Frequency domain analysis and plots
• JPEG compression artifacts analysis
• File format verification

Any anomalies or indications of manipulation will be highlighted in the report.

Credits

Nightshade Antidote was created by Richard Aragon. The code implements common digital image forensics techniques based on research papers and books in the field.