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  • Data Transfer Protocols
  • Encryption Details
  • AI Implementation
  • Security Features
  • Compatibility
  • Open-Source Components
  • Performance Metrics
  1. Selenium AI Meetings

Technical details

This section provides an in-depth look at the technical specifications, protocols, and implementation details of Selenium AI Call.

Data Transfer Protocols

  • WebRTC with Modifications: Used for low-latency audio and video calls, with optimizations to achieve <100ms latency. Modifications include custom packet prioritization and jitter buffer tuning.

  • Matrix Protocol: Enables interoperability with other messengers (e.g., Signal, Telegram) by supporting federated messaging and encryption standards.

Encryption Details

  • Signal Protocol: Implements E2E encryption with Perfect Forward Secrecy using Curve25519 for key exchange and AES-256 for data encryption.

  • CRYSTALS-Kyber (PQ3): Post-quantum encryption based on lattice cryptography, implemented in Rust for performance.

  • ZRTP: Dynamic key sharing for audio/video calls, using SHA-256 for key derivation.

  • AES-256-GCM: Encrypts attachments and sensitive data, with hardware acceleration where available.

AI Implementation

  • Noise Reduction: RNNoise neural network, implemented in C++ for speed, processes 48kHz audio frames to suppress background noise.

  • Echo Suppression: Combines adaptive filters (C++) and deep learning (Python) to eliminate echo in real-time.

  • Autotranslation: OpenAI’s Whisper model, running on-device in Python, supports 30+ languages with <500ms latency.

  • Voice Correction: AI model (C++) fills pauses and removes stutters by analyzing speech patterns.

  • Deepfake Detector: ResNet50-based neural network (C++ with OpenCV) analyzes video frames for artifacts, achieving >95% accuracy.

Security Features

  • Local Rendering: All AI processing occurs on-device, using TensorFlow Lite or ONNX for efficient inference.

  • Anonymous Sessions: Tor-based routing with one-time identifiers, implemented in Rust for reliability.

  • Biometric Authentication: Combines face_recognition (Python) and speech_recognition (Python) for secure login.

  • Digital Watermarks: Embeds 16-byte markers in audio/video streams, verified using Rust-based algorithms.

Compatibility

  • Platforms: Supports desktop (Windows, macOS, Linux) and mobile (iOS, Android) via React Native for mobile builds.

  • Messengers: Integrates with Matrix-compatible platforms, allowing cross-app communication.

Open-Source Components

  • Encryption Kernel: Signal Protocol and CRYSTALS-Kyber implementations (Rust) are publicly auditable.

  • Deepfake Detector: ResNet50 model and inference code (C++) are available for community review.

Performance Metrics

  • Call Latency: <100ms (WebRTC optimizations).

  • Translation Latency: <500ms for 30-second audio clips.

  • Deepfake Detection: Processes 30fps video with <50ms per frame.

  • Encryption Overhead: <10ms for AES-256-GCM encryption of 1MB attachments.

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Last updated 1 month ago

GitHub Repository: Hosted at , with detailed contribution guidelines.

https://github.com/SeleniumFinance/SeleniumAICall