Deepfake Audio Detection — spot AI-cloned voices in vishing attacks
Defensive security: detect AI-generated deepfake audio used in voice phishing (vishing) — extract spectral features (MFCC, spectral centroid, contrast, zero-crossing rate) and classify samples with ML models, with batch analysis, confidence scoring, and forensic reporting
- What
- Defensive security: detect AI-generated deepfake audio used in voice phishing (vishing) — extract spectral features (MFCC, spectral centroid, contrast, zero-crossing rate) and classify samples with ML models, with batch analysis, confidence scoring, and forensic reporting
- Cost
- Free
- Needs
- Python 3.9+ with librosa, numpy, scikit-learn, and scipy installed; FFmpeg installed for audio format conversion; audio samples in WAV, MP3, or FLAC format (minimum 3 seconds); optionally a reference corpus of genuine voice samples for the targeted individual
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @mukul975's detecting-deepfake-audio-in-vishing-attacks skill, listed here with credit to its creator (authored by mukul975, purely defensive fraud/soc work): detect AI-generated deepfake audio used in voice phishing by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. It ships the full workflow — audio preprocessing (resample, trim, normalize with librosa), feature extraction, batch audio analysis, confidence scoring, and forensic reporting — for use cases like investigating a suspected AI-cloned executive voice authorizing a wire transfer, validating a voicemail that sounds like the CEO but feels off, or giving blue teams detection capability against red-team voice cloning. Mapped to MITRE ATT&CK, MITRE ATLAS, D3FEND, and NIST frameworks. Honest caveats: detection models are only as good as their training data — a reference corpus of genuine voice samples improves accuracy; deepfake generators improve constantly, so detection is an arms race; audio evidence needs proper chain of custody for legal use. Apache-2.0 licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3.9+ with librosa, numpy, scikit-learn, and scipy installed; FFmpeg installed for audio format conversion; audio samples in WAV, MP3, or FLAC format (minimum 3 seconds); optionally a reference corpus of genuine voice samples for the targeted individual Install "Deepfake Audio Detection — spot AI-cloned voices in vishing attacks" for me. It gives my agent @mukul975's defensive deepfake-audio detection workflow: preprocess audio (resample to 16kHz, trim silence, normalize), extract spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate), classify samples with ML models, run batch audio analysis with confidence scoring, and produce forensic reports — mapped to MITRE ATT&CK, ATLAS, D3FEND, and NIST frameworks. Apache-2.0 licensed. Repository: https://github.com/mukul975/anthropic-cybersecurity-skills/blob/main/skills/detecting-deepfake-audio-in-vishing-attacks/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "detecting-deepfake-audio-in-vishing-attacks". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. pip install librosa numpy scikit-learn scipy; install FFmpeg; prepare suspect audio samples of 3+ seconds; optionally gather a reference corpus of genuine voice samples). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.