🛡️ Defakepy: Open Source Deepfake & AI Detection Library

Defakepy is a powerful, open-source forensic suite for detecting AI-generated content across text, audio, and video. It uses an ensemble of statistical, biological, and cryptographic techniques to produce a human-readable Trust Score.

⚖️ Disclaimer: Detection is probabilistic, not absolute. Defakepy is a tool to assist human judgment, not replace it.

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✨ Key Features

Feature	Method	Detects
Text Forensics	RoBERTa binary classifier	LLM-generated prose (GPT-4, Claude, Llama)
Image Analysis	ViT (Vision Transformer) classifier	AI-generated images (Midjourney, DALL-E, SD)
Video Analysis	Eye Aspect Ratio (EAR)	Unnatural blinking in face videos
Audio Forensics	MFCC Spectral Analysis	AI voice cloning & synthetic speech
Provenance Check	C2PA Metadata	Missing/invalid digital watermarks

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🚀 Installation

Defakepy is "Lite by Default" — the core package is tiny and installs instantly. Install only the features you need.

Core (Instant)

pip install defakepy

Feature Extras

Install only what your use case requires:

Extra	Command	What it enables
Text	pip install "defakepy[text]"	torch + transformers for text AI detection
Image	pip install "defakepy[image]"	ViT classifier for AI image detection
Audio	pip install "defakepy[audio]"	librosa + scipy for cloned voice detection
Vision	pip install "defakepy[vision]"	dlib + opencv for deepfake video detection
Provenance	pip install "defakepy[provenance]"	c2pa-python for digital signature checks
Full	pip install "defakepy[full]"	Everything above combined

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� Usage

1. Python API

Analyze a String of Text

from defakepy import ForensicScanner

scanner = ForensicScanner()

text = """Artificial intelligence has rapidly transformed numerous industries,
offering unprecedented efficiencies and capabilities that were unimaginable before."""

report = scanner.text_engine.analyze(text)

print(f"AI Generated:    {report['is_ai']}")
print(f"AI Probability:  {report['ai_probability'] * 100:.1f}%")  # e.g., 87.3%
print(f"Burstiness:      {report['burstiness']:.2f}")  # Sentence variety
print(f"Confidence:      {report['confidence']}%")

Analyze an Image File

scanner = ForensicScanner()
report = scanner.image_engine.analyze("suspicious_photo.jpg")

print(f"AI Generated:   {report['is_ai']}")
print(f"AI Probability: {report['ai_probability'] * 100:.1f}%")
print(f"Confidence:     {report['confidence']}%")

Analyze an Audio File

scanner = ForensicScanner()
report = scanner.audio_engine.analyze("voice_clip.wav")

print(f"Synthetic Voice: {report['is_synthetic']}")
print(f"Confidence:      {report['confidence']}%")

Analyze a Video File

scanner = ForensicScanner()
report = scanner.vision_engine.analyze("interview.mp4")

print(f"Suspicious:  {report['is_suspicious']}")
print(f"Blink Count: {report['blink_count']}")
print(f"Confidence:  {report['confidence']}%")

All-in-One File Scan

Automatically detects file type and runs the appropriate engine:

scanner = ForensicScanner()
report = scanner.scan_file("suspicious_video.mp4")

print(f"Trust Score: {report['trust_score']}/100")
print(f"Status:      {'CLEAR' if report['trust_score'] > 70 else 'SUSPICIOUS'}")
for flag in report.get('flags', []):
    print(f"  [!] {flag}")

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2. Command-Line Interface (CLI)

Basic Scan

defakepy-scan --input suspicious_video.mp4

Output as JSON (for scripting/automation)

defakepy-scan --input my_article.txt --json

Check Version

defakepy-scan --version

Example CLI Output

========================================
🛡️  DEFAKEPY FORENSIC REPORT
========================================
File:        suspicious_video.mp4
Type:        VIDEO
Provenance:  Unsigned
Trust Score: 45/100
----------------------------------------
STATUS:      SUSPICIOUS

FLAGS:
  [!] Biological Anomaly: Low/Zero Blink Rate
  [!] Audio Anomaly: Synthetic Spectral Signature
  [!] Metadata Trace: Missing C2PA/Digital Signature
========================================

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📊 Understanding the Output

Trust Score

• > 70 → ✅ CLEAR: No significant anomalies found.
• 40–70 → ⚠️ SUSPICIOUS: One or more anomalies detected. Review recommended.
• < 40 → 🚨 CRITICAL: Multiple strong indicators of AI generation or manipulation.

Text Analysis: ai_probability

The text engine returns an ai_probability score (0.0 = human, 1.0 = AI), powered by a fine-tuned RoBERTa classifier (Hello-SimpleAI/chatgpt-detector-roberta). A result > 60% is flagged as AI.

⚠️ Limitation: AI text detection is still an open research problem. Adversarial prompting (e.g., "rewrite this to sound more human"), paraphrasing tools, or fine-tuned models can evade any classifier. Treat the result as a signal to investigate further, not a verdict.

Flags Explained

Flag	What it Means
Biological Anomaly: Low/Zero Blink Rate	A real human blinks 15–20 times per minute. AI-generated videos often have zero or near-zero blinks.
Audio Anomaly: Synthetic Spectral Signature	Real voices have natural variation and imperfections. Cloned voices are spectrally "too smooth."
Metadata Trace: Missing C2PA/Digital Signature	Authentic media from cameras/publishers carries a C2PA watermark. Its absence is a weak signal but worth flagging.

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🛠️ Troubleshooting

ReadTimeoutError during installation

Your network timed out while downloading a large file. Run with an extended timeout:

pip install --default-timeout=1000 "defakepy[full]"

dlib build failure on Windows

dlib requires CMake and a C++ compiler. Install CMake from cmake.org, select "Add CMake to the system PATH", restart your terminal, then try again.

ImportError: torch is not installed

You're using the text engine without installing the text extra. Fix:

pip install "defakepy[text]"

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🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md to get started.

📄 License

Distributed under the MIT License. See LICENSE for details.