AVID — Audio-Visual Identity Detection by realxreal.ai
Absolutely — here’s your rewritten README.md with the updated name:
# 🎛️ rxr-avid
**rxr-avid** is a Python-based research toolkit for **deepfake detection via timing analysis**, built by **[realxreal.ai](https://realxreal.ai)** to expose subtle artifacts in AI-generated audio/video.
> **A.V.I.D.** stands for:
> **A**udio **V**erification via **I**mplicit **D**ynamics
This module draws inspiration from **cryptographic timing attacks**, **entropy modeling**, and **covert channel detection** to uncover what deepfakes can't fake — real-time human behavior.
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## 🎯 Purpose
Deepfakes often **sound real** — but they rarely **behave real**.
**rxr-avid** analyzes the **micro-dynamics** of audio to detect:
- Hidden regularity in synthetic voices
- Robotic timing in pre-rendered responses
- Absence of real-time decision-making variability
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## 🔍 What It Detects
- 🔄 **Unnatural latency** between words or phrases
- 🧯 **Consistently low entropy** in speech pauses
- 🎛️ **Synthesis compression patterns** in time domain
- 🧠 **Timing artifacts** of scripted, non-interactive voice AI
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## 🔬 How It Works (WIP)
- 🎧 **Audio Ingestion**
Loads speech recordings (live or saved)
- ⏱️ **Timing Feature Extraction**
Captures:
- Word-level time deltas
- Breath-to-speech cadence
- Entropy of pauses and pacing
- Jitter and variation across utterances
- 📊 **Behavioral Modeling**
Compares features against:
- Human variability benchmarks
- Known synthetic voice baselines
- Dynamic thresholds based on context
- ⚠️ **Anomaly Detection**
Highlights patterns likely indicative of deepfakes or scripted synthesis
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## 📦 Install
```bash
pip install rxr-avid
⚠️ Early research release. Requires Python 3.8+,librosa,scipy,numpy,soundfile, etc.
- 🔐 Timing attacks in cryptography (e.g., RSA key leak through latency)
- 🎤 Real-time speech cognition under pressure
- 🎛️ Audio-based covert channels and side-channel detection
- 🧪 Behavioral forensics from security & psycholinguistics
from rxr_avid import TimingAnalyzer
analyzer = TimingAnalyzer("alice_speech.wav")
results = analyzer.analyze()
print(results["entropy_score"])
print(results["jitter_variance"])
print(results["flags"]) # e.g., ["uniform_pause", "low_variance"]- ✅ Multi-layer trust protocols
- 🔒 Audio challenge-response authentication
- 📁 Audio forensics and threat detection
- 🧬 Physiological acoustic fingerprinting (future)