Detect fake FLAC files — find MP3/AAC/Opus transcodes disguised as lossless audio. Fast Rust CLI + web UI.
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Updated
Aug 19, 2026 - Rust
Detect fake FLAC files — find MP3/AAC/Opus transcodes disguised as lossless audio. Fast Rust CLI + web UI.
Ensemble speaker verification achieving 97% accuracy - Intelligent fusion of MFCC+DTW (92%) and Resemblyzer CNN (94%) for voice authentication
Audio analysis toolkit
Desktop GUI tool for detecting audio deepfakes using hand-crafted features + XGBoost/LightGBM ensemble. Segment-level analysis, heatmaps, PDF reports. Zero deep learning required.
A web app that detects deepfake voices using an LLM forensic analyzer.
Detecta manipulaciones en grabaciones de audio con IA: localiza el empalme y genera un informe pericial con hash SHA-256
Cross-platform audio quality and integrity analysis with lightweight Quick Inspect, deep evidence, and the Oracle Engine.
Free, local-first and explainable AI-origin music analysis for artists, labels, platforms, rights teams and researchers.
Local-first audio forensic analyzer for spectral cutoffs, transcodes, clipping and discontinuities.
A robust AI-powered web app to detect deepfake audio using Wav2Vec2 and FastAPI. Deployed on Hugging Face Spaces for the CyberSecurity Hackathon.
Forensic toolkit for detecting AI-synthetic voices through Nonlinear Phenomena (NLP) and phase-space reconstruction.
Full-stack forensic speech analysis platform for detecting AI-generated vs human audio with real-time inference and explainable ML.
A FLAC steganography toolkit for encrypted payloads, audio forensics, and PCM embedding benchmarks.
Explainable deepfake voice detection — RawNet2 + Whisper + real-time browser inference. 95.2% accuracy, 4.19% EER on ASVspoof 2019.
A web-based Audio Forensic Intelligence Platform for audio authenticity verification, fingerprint generation, tamper detection, watermark validation, and forensic report generation using Django and Librosa.
AI voice security platform detecting synthetic speech & voice cloning attacks, fusing WavLM transformer embeddings + LFCC forensics + LightGBM — 99.84% accuracy.
Forensic Audio Classifier Tool is an ML-based digital forensics system built using PyTorch, Transformers, and a custom hybrid pipeline (Acoustic Model + Language Model + Classifier). It is designed for the Tripura Bengali dialect, enabling accurate transcription, keyword detection, and automated (Flagged / Review / Safe) audio classification.
Deepfake audio detection thesis: ML & deep learning models with live benchmark & Streamlit demo
A Decoupled Tri-Modal Architecture for Real-Time Deepfake Interception via WebRTC and Late-Fusion.
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