pablocaeg/sloptotal

Open-source AI text detector — VirusTotal for AI slop. 23 engines, self-hosted, runs on CPU. Scan text or URLs locally. - pablocaeg/sloptotal

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README

SlopTotal VirusTotal for AI-generated text. Paste text, drop in a PDF or Word file, or give it a URL. Twenty-three independent AI detectors (neural classifiers, statistical tests and linguistic heuristics) score it in parallel, and a calibrated ensemble turns their votes into one verdict you can inspect engine by engine. It runs on your own CPU, so nothing you scan leaves your machine. It is a free, self-hosted, open-source alternative to hosted AI content detectors such as GPTZero, Originality.ai, Copyleaks, ZeroGPT and Humalingo. Instead of one number from one model, it shows you every model's opinion, and it publishes how accurate that is, failures included.

Try it: sloptotal.com · Run it: docker run -p 8000:8000 ghcr.io/pablocaeg/sloptotal Features

23 detection engines, one calibrated score. DeBERTa and RoBERTa classifiers, Binoculars, Fast-DetectGPT, GLTR, perplexity and burstiness tests, and stock-phrase heuristics. Results stream in as each engine finishes. Text, URLs and documents. Paste text, scan a web page (main content is extracted automatically), or upload .pdf, .docx, .txt or .md. Site check: was this website vibe-coded? Finds the fingerprints that Lovable, v0, Bolt, Base44, Replit and Same leave in the sites they deploy, and shows the evidence for each one. How it works Per-paragraph heat map through the API, to see which parts read as AI. Measured, not claimed. Every accuracy number below comes with the corpus, the harness and the raw per-sample scores. Private by default. Self-hosted, no third-party AI APIs, no tracking, reports deleted after 30 days. CPU-only is fine. Auto-detects your hardware; 4 GB RAM is enough for the lite profile, a GPU is optional. JSON API and a Chrome extension that marks AI-looking results in Google Search and LinkedIn.

Measured accuracy Most detectors publish an accuracy figure without saying what it was measured on. These numbers, the harness that produced them and the raw per-sample results are all in tests/eval/. Two corpora, deliberately:

Corpus What Size

Multi-domain RAID: news, book prose, poetry, academic abstracts. AI from GPT-4, ChatGPT, Llama, Mistral, Cohere, GPT-3 110 (40 human, 70 AI)

Literary control Project Gutenberg prose published 1532-1915 -- Machiavelli, Austen, Melville, Kafka 26 (all human)

The second exists because a high score there cannot be anything but an error: the writing predates language models by a century or more. Optimising on the first corpus alone produces a threshold that mislabels literature.

Result

Overall AUC 0.974

AI reaching "Suspicious" or above 90%

Human text wrongly called "Likely AI" 1 of 66

Literary passages flagged 0 of 26

Re-measured in September 2026 on a fresh RAID sample (180 texts) with upgraded dependencies: AUC 0.979, 1 of 40 human texts called "Likely AI", 0 of 26 literary passages flagged. What does not work. Short text is unreliable below roughly 80 words and settles from about 200. Hand-edited AI loses fingerprints with every rewriting pass. Source code is outside what these engines do: in testing they never falsely accused human code, and never caught machine-written code either -- so we do not claim they can. The failures are published too, including three engines found scoring backwards and two loading a randomly initialised network while carrying real ensemble weight. Read them at sloptotal.com/detect/ai-detector-benchmark/ and sloptotal.com/detect/ai-detector-false-positives/. Quick start Docker (fastest) docker run -p 8000:8000 -v sloptotal-models:/app/models ghcr.io/pablocaeg/sloptotal Open http://localhost:8000. The first scan downloads about 2 GB of models into the sloptotal-models volume, so later starts are quick. To build from source instead, run docker compose up. From source Requires Python 3.10+ (macOS ships 3.9, which is too old). git clone https://github.com/pablocaeg/sloptotal.git cd sloptotal python3.11 -m venv venv && source venv/bin/activate pip install -r requirements.txt ./start.sh # or: uvicorn app.main:app --port 8000 Check that every engine loads and scores, end to end: python scripts/smoke_test.py # against http://localhost:8000 Site check: detect sites built with AI app builders

"Is this website vibe-coded?" checkers mostly score style (Tailwind class counts, missing security headers, buzzwords) and turn it into a percentage. Hand-written sites share all of those traits. SlopTotal looks only for markers the builders themselves leave in what they deploy, each one confirmed on live sites or in the builders' own templates:

Builder Fingerprints

Lovable gptengineer.js runtime, /lovable-uploads/ assets, the Lovable badge, /~flock.js, *.lovable.app

v0 (Vercel) <meta name="generator" content="v0.app"> from v0's layout template, *.vusercontent.net

Bolt X-Powered-By: Bolt.new header, bolt.new/badge.js, *.bolt.host

Base44 app.base44.com platform calls, base44_access_token, *.base44.app

Replit Replit Agent dev banner, Replit badge, *.replit.app

Same assets served from same-assets.com

A site with no marker may still have been written with AI: code exported from these tools and hosted elsewhere, or written in an AI editor, carries no fingerprint. So the result is evidence, not a probability. The page's copy is scored separately by the text engines. curl -X POST http://localhost:8000/api/scan/site \ -H "Content-Type: application/json" -d '{"url": "example.com"}' API

Endpoint Method What it does Typical latency (CPU)

/api/analyze POST Full 23-engine report for text or url 2-8 s

/api/quick-score POST 4 classifiers plus heuristics 0.1-0.5 s

/api/paragraph-score POST Score per paragraph (heat map) 1-3 s

/api/scan/site POST AI app builder fingerprints plus a copy score 1-3 s

/api/extract POST Text from an uploaded .pdf / .docx / .txt (multipart file) < 1 s

/api/scan/snippets POST Batch of 1-30 short snippets ~0.5 s

/api/scan/urls POST Batch of 1-10 URLs, page-type aware 1-5 s

/api/engines GET Engine metadata instant

/api/report/{id} GET A stored report instant

/api/queue/status GET Queue capacity instant

curl -X POST http://localhost:8000/api/analyze \ -H "Content-Type: application/json" \ -d '{"text": "Your text to analyze here..."}' The response lists every engine with its score, verdict and a plain-language detail line, plus overall_score (0-100) and overall_verdict. Detection Engines Every engine links to its page on sloptotal.com, which carries its measured scores against both corpora. AUC below is the probability the engine ranks a random AI passage above a random human one: 1.0 is perfect, 0.5 is a coin flip. Neural Classifiers

Engine Model AUC Notes

Desklib DeBERTa DeBERTa-v3-large (435M) 1.000 Strongest separation in our own tests

SuperAnnotate RoBERTa-large (355M) 0.989 No measurable bias against archaic prose

E5-Small E5 + LoRA (33M) 0.999 Matches far larger models at 33M params

TMR Detector RoBERTa-base (125M) 1.000 RAID-trained, so RAID scores flatter it

BERT-tiny RAID BERT-tiny (4.4M) 1.000 Answers in milliseconds

ReMoDetect DeBERTa (184M) 0.941 Targets RLHF-aligned LLMs

ChatGPT Detector RoBERTa-base (125M) 0.829 ChatGPT-specific

Fakespot RoBERTa-base (125M) 0.999 Accurate on modern text, but +0.533 bias on pre-1920 prose

OpenAI Detector RoBERTa-base (125M) 0.771 The 2019 GPT-2 detector; weaker on modern LLMs

Statistical Methods

Engine Method AUC

Log-Rank Average log-rank under GPT-2 0.909

GLTR Token rank distribution 0.904

Perplexity GPT-2 perplexity scoring 0.901

Cross-Perplexity Two-model perplexity comparison 0.891

Fast-DetectGPT Conditional probability curvature 0.890

Binoculars Cross-entropy ratio between two LMs 0.836

DivEye Surprisal diversity 0.730

Linguistic Heuristics

Engine Signal AUC

Structural Analysis Em-dash usage, sentence uniformity 0.836

Linguistic Markers AI-preferred phrases ("delve", "tapestry"...) 0.713

Formulaic Patterns Cliche openings and closings 0.698

Vocabulary Richness Type-token ratio, hapax legomena 0.583

Readability Uniformity Cross-paragraph consistency 0.581

Burstiness Per-sentence perplexity variance 0.582

Sentiment & Hedging Hedging and forced balance 0.522

The linguistic heuristics are weak on their own. They are kept because they fail independently of the neural classifiers, which is what makes them useful as tiebreakers rather than as evidence. Scoring The final score is calibrated, not a simple average, and every weight is derived from measurement rather than intuition. See tests/eval/FINDINGS.md and sloptotal.com/detect/ai-detector-ensemble/.

Anchored on the unbiased classifiers -- Desklib, SuperAnnotate, E5 and ReMoDetect all score high AUC with no measurable bias against older prose. Their consensus is blended 60/40 with the full weighted set. Weights from measurement -- each engine's share is proportional to Somers' D (2*AUC - 1), scaled down by any bias it shows against archaic writing. RAID-trained engines are damped because our corpus is RAID. Confidence from agreement -- a tight cluster across independent engine families is trustworthy; one confident engine is not. Skepticism, but only when earned -- unanimous high classifier scores are damped only when the text itself carries human markers (contractions, first-person, slang). Applied unconditionally it fired on 69 of 70 AI samples and 0 of 66 human ones, suppressing correct detections.

Fakespot was previously the anchor, weighted 0.13. It is accurate on modern text (AUC 0.999) but scored pre-1920 human prose at 0.645 against 0.112 for modern human writing -- the largest bias of any engine -- and anchoring amplified it. Machiavelli scored 62.5. After demotion to 0.033, literary passages average 10.2 and none is flagged. Configuration SlopTotal detects CPU, RAM and GPU at startup and picks a profile. Everything can be overridden with environment variables; see .env.example.

Profile RAM CPU GPU Notes

Lite 4 GB 2 cores None All engines, slower

Standard 8 GB 4 cores None Default for most laptops

Performance 16 GB+ 6+ cores CUDA optional Pool replicas, max throughput

High-RAM CPU servers (e.g. 64 GB, no GPU): you automatically get the performance profile. With no CUDA, all inference stays on CPU but you can run more concurrent workers and model pool replicas:

Tune for a 64 GB CPU-only server

Variable Default Purpose

SLOPTOTAL_PROFILE auto lite, standard or performance

SLOPTOTAL_RETENTION_DAYS 30 Delete reports after N days (0 keeps them)

SLOPTOTAL_ALLOW_PRIVATE_URLS off Let URL scans reach private or intranet hosts (blocked by default)

HF_HOME ./models Where model weights are cached

FAQ Can AI detectors be trusted? Not blindly. No detector, this one included, should be the only evidence for an accusation. That is why SlopTotal shows all 23 votes, how much they agree, and its measured false-positive rate. Short text (under about 80 words) and heavily edited AI text are unreliable for every detector. Does it detect ChatGPT, Claude, Gemini, Llama and Mistral? The evaluation corpus includes GPT-4, ChatGPT, Llama and Mistral output. The classifiers were trained on a wider mix. Newer models are covered as far as they share those fingerprints; the evaluation harness lets you measure any model you care about. Will it flag classic literature or formal writing? Not in our tests: none of the 26 passages from Austen, Melville, Kafka, Machiavelli and others is flagged. Pre-1920 prose is part of the evaluation precisely because naive detectors fail on it. Is my text stored or shared? It is processed on the server you run. Reports are kept for 30 days (configurable) so report links work, and nothing is sent to an outside service. Can it detect AI-generated code? No, and we do not claim it can: in testing the engines never flagged human code but never caught machine-written code either. The Site check reports which AI app builder produced a website, which is a different question. Troubleshooting

Symptom Fix

TypeError: unsupported operand type(s) for | at startup Python 3.9 or older; use 3.10+

An engine reports Model loading failed Check disk space and network for the first model download, then restart; python scripts/smoke_test.py shows which engine fails

First scan is slow Models are loading; later scans take seconds

A URL scan says "private network address" Intended; set SLOPTOTAL_ALLOW_PRIVATE_URLS=1 to scan intranet pages

Project layout app/ FastAPI backend: engines, ensemble, site fingerprints, API web/ The web UI (Jinja2 templates, vanilla JS, no build step) tests/ Unit tests (seconds, no downloads) and tests/eval/ accuracy harness scripts/ smoke_test.py (end-to-end) and the model drift check benchmarks/ Speed and load scripts

ARCHITECTURE.md covers the internals. AGENTS.md is a short brief for contributors and AI coding assistants. Newer open detectors we measured Detector models keep appearing on Hugging Face, each with its own accuracy claim. Before adding any, we score them on the same two corpora. September 2026, standalone, 180 RAID texts plus the 26 literary passages:

Model RAID AUC Literary bias (lower is better) Status

Gradient (DeBERTa-v3-large) 0.998 0.033 Next engine to add

Vanguard (ModernBERT-large) 0.998 0.037 Candidate; poorly calibrated at 0.5

Earlybird-fast (82M) 0.913 0.040 Candidate for fast snippet scans

rasbt ModernBERT 0.864 0.000 Not added

RAID-trained models score near 1.0 on RAID by construction and need a different test set first. The full table, the models we excluded and why, and the raw scores are in tests/eval/FINDINGS.md. The roadmap is in TODO.md. Related projects and reading

RAID benchmark (ACL 2024): adversarial AI text detection dataset used in our evaluation Detecting the Machine (2026): cross-architecture detector benchmark; ensembles beat single detectors EditLens: human vs AI-edited vs AI-generated classification GLTR: visual token-rank inspection, the inspiration for our GLTR engine distil-labs/distil-ai-slop-detector: a 270M Gemma detector that runs in the browser sloptotal-extension: the Chrome extension

Contributing Contributions are welcome, especially new engines with measurements. Start with CONTRIBUTING.md.

Report a bug Request a feature Propose a new engine

If SlopTotal is useful to you, a star helps other people find it. License MIT. Model weights keep their own licenses; see THIRD_PARTY_LICENSES.md.

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