Every creator platform without AI detection is hemorrhaging trust. I built this after watching a mid-size writing marketplace get flooded with GPT-generated essays that gamed their system for six weeks. The human writers lost visibility to content that took seconds to produce. We needed detection that ran on CPU, handled 500+ submissions per hour, and didn't depend on external APIs.
Here's the full build.
Why Platforms Are Ranking Human Content Higher
Human writing has statistical fingerprints that LLMs struggle to replicate. Burstiness—the variance in sentence length—is much higher in human text. One short sentence. Then a longer, complex one with a subordinate clause that trails into something almost philosophical. LLMs normalize this variance.
There's also perplexity: how "surprising" the text is to a language model. AI-generated text scores low perplexity because it consistently picks high-probability tokens. Human writing is weirder, more idiosyncratic, harder to predict.
Medium and Substack already quietly penalize low-burstiness content in their recommendation algorithms. Building this into your ingestion pipeline is no longer optional if you care about creator trust.
Building Your Authenticity Scoring Engine
The core is a ContentAnalyzer class that computes four signals: perplexity score, burstiness, lexical diversity, and punctuation entropy. None require GPU inference—they run in milliseconds as pure statistical computation.
python
import math
import re
import string
from collections import Counter
from dataclasses import dataclass
from typing import List
@dataclass
class AuthenticityScore:
perplexity_proxy: float
burstiness: float
lexical_diversity: float
punctuation_entropy: float
composite_score: float # 0.0 (likely AI) to 1.0 (likely human)
flagged: bool
class ContentAnalyzer:
def init(self, flag_threshold: float = 0.35):
self.flag_threshold = flag_threshold
def _sentence_lengths(self, text: str) -> List[int]:
sentences = re.split(r'(?<=[.!?])\s+', text.strip())
return [len(s.split()) for s in sentences if len(s.split()) > 2]
def _burstiness(self, lengths: List[int]) -> float:
if len(lengths) < 2:
return 0.0
mean = sum(lengths) / len(lengths)
variance = sum((l - mean) ** 2 for l in lengths) / len(lengths)
std_dev = math.sqrt(variance)
# Coefficient of variation—AI text clusters around 0.3-0.5
return std_dev / mean if mean > 0 else 0.0
def _lexical_diversity(self, text: str) -> float:
words = re.findall(r'\b[a-z]+\b', text.lower())
if not words:
return 0.0
return len(set(words)) / len(words)
def _punctuation_entropy(self, text: str) -> float:
punct = [c for c in text if c in string.punctuation]
if not punct:
return 0.0
counts = Counter(punct)
total = len(punct)
entropy = -sum((c / total) * math.log2(c / total) for c in counts.values())
return entropy
def _perplexity_proxy(self, text: str) -> float:
# Bigram-based approximation without a full LM
words = re.findall(r'\b[a-z]+\b', text.lower())
if len(words) < 10:
return 0.5
bigrams = [(words[i], words[i+1]) for i in range(len(words)-1)]
bigram_counts = Counter(bigrams)
unigram_counts = Counter(words)
log_prob = 0.0
for (w1, w2), count in bigram_counts.items():
p = count / unigram_counts[w1]
log_prob += math.log2(p) * count
# Normalize and invert: lower raw = higher perplexity proxy
avg_log_prob = log_prob / len(bigrams)
return min(1.0, max(0.0, (-avg_log_prob) / 10.0))
def analyze(self, text: str) -> AuthenticityScore:
lengths = self._sentence_lengths(text)
burst = self._burstiness(lengths)
lex_div = self._lexical_diversity(text)
punct_ent = self._punctuation_entropy(text)
perp = self._perplexity_proxy(text)
# Weighted composite: higher = more human-like
composite = (
burst * 0.35 +
lex_div * 0.25 +
min(punct_ent / 3.0, 1.0) * 0.20 +
perp * 0.20
)
composite = min(1.0, max(0.0, composite))
return AuthenticityScore(
perplexity_proxy=round(perp, 4),
burstiness=round(burst, 4),
lexical_diversity=round(lex_div, 4),
punctuation_entropy=round(punct_ent, 4),
composite_score=round(composite, 4),
flagged=composite < self.flag_threshold
)
This gives a fast, interpretable baseline. The composite_score weights burstiness heaviest because it's the hardest signal for LLMs to fake without explicit prompting. Anything below 0.35 gets flagged.
Adding ML-Based Pattern Detection
Statistical signals alone hit 71% accuracy. To push past that, add roberta-base-openai-detector from Hugging Face—trained on GPT-2 output but generalizes well to GPT-3.5+.
Install dependencies:
bash
pip install fastapi uvicorn transformers torch sentencepiece pydantic python-dotenv
Wrap the model in an MLDetector class that caches the pipeline on init. Do not reload it per request.
python
from transformers import pipeline
class MLDetector:
_instance = None
def __init__(self, model_name: str = "roberta-base-openai-detector"):
print(f"Loading model: {model_name}")
self.classifier = pipeline(
"text-classification",
model=model_name,
truncation=True,
max_length=512
)
@classmethod
def get_instance(cls) -> "MLDetector":
if cls._instance is None:
cls._instance = cls()
return cls._instance
def predict(self, text: str) -> dict:
# Truncate to avoid token limit issues
truncated = text[:2000]
result = self.classifier(truncated)[0]
label = result["label"].lower()
confidence = result["score"]
# Model outputs "LABEL_1" for AI, "LABEL_0" for human
is_ai = label in ("fake", "label_1")
return {
"ml_prediction": "ai" if is_ai else "human",
"ml_confidence": round(confidence, 4),
"ml_flagged": is_ai and confidence > 0.75
}
I hit one real bug on first deployment: loading MLDetector inside the route handler meant a 12-second cold start on every request. The fix: singleton pattern + FastAPI startup event to pre-warm the model when the server boots.
Creating the REST API
The /analyze endpoint accepts a POST with content_id and text. Returns the full breakdown plus a final verdict.
python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import Optional
import time
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(name)
app = FastAPI(title="Content Authenticity API", version="1.0.0")
@app.on_event("startup")
async def startup_event():
logger.info("Pre-warming ML model...")
MLDetector.get_instance()
logger.info("Model ready.")
class SubmissionRequest(BaseModel):
content_id: str = Field(..., description="Platform content identifier")
text: str = Field(..., min_length=50, description="Content body to analyze")
author_id: Optional[str] = None
class AnalysisResponse(BaseModel):
content_id: str
composite_score: float
ml_prediction: str
ml_confidence: float
burstiness: float
lexical_diversity: float
punctuation_entropy: float
perplexity_proxy: float
verdict: str # "PASS", "REVIEW", "REJECT"
flagged: bool
processing_ms: float
def get_verdict(stat_flagged: bool, ml_flagged: bool, composite: float) -> str:
if stat_flagged and ml_flagged:
return "REJECT"
if stat_flagged or ml_flagged or composite < 0.45:
return "REVIEW"
return "PASS"
@app.post("/analyze", response_model=AnalysisResponse)
async def analyze_content(submission: SubmissionRequest):
start = time.monotonic()
if len(submission.text.split()) < 20:
raise HTTPException(
status_code=422,
detail="Text too short for reliable analysis (minimum 20 words)"
)
analyzer = ContentAnalyzer(flag_threshold=0.35)
stat_result = analyzer.analyze(submission.text)
detector = MLDetector.get_instance()
ml_result = detector.predict(submission.text)
verdict = get_verdict(
stat_flagged=stat_result.flagged,
ml_flagged=ml_result["ml_flagged"],
composite=stat_result.composite_score
)
elapsed_ms = (time.monotonic() - start) * 1000
logger.info(
f"content_id={submission.content_id} verdict={verdict} "
f"composite={stat_result.composite_score} "
f"ml_confidence={ml_result['ml_confidence']} "
f"ms={elapsed_ms:.1f}"
)
return AnalysisResponse(
content_id=submission.content_id,
composite_score=stat_result.composite_score,
ml_prediction=ml_result["ml_prediction"],
ml_confidence=ml_result["ml_confidence"],
burstiness=stat_result.burstiness,
lexical_diversity=stat_result.lexical_diversity,
punctuation_entropy=stat_result.punctuation_entropy,
perplexity_proxy=stat_result.perplexity_proxy,
verdict=verdict,
flagged=verdict != "PASS",
processing_ms=round(elapsed_ms, 2)
)
@app.get("/health")
async def health():
return {"status": "ok", "model_loaded": MLDetector._instance is not None}
The three-tier verdict system (PASS / REVIEW / REJECT) is intentional. Auto-rejecting borderline content kills legitimate writers who write cleanly. REVIEW routes to a human moderator queue. Only REJECT blocks publication.
Run locally:
bash
uvicorn main:app --host 0.0.0.0 --port 8000 --workers 1
Test with curl:
bash
curl -X POST http://localhost:8000/analyze \
-H "Content-Type: application/json" \
-d '{"content_id": "post_001", "text": "Your article text goes here..."}'
Scaling Without GPU Costs
The roberta-base-openai-detector runs on CPU at 180-300ms per request on a t3.medium. That works for async pipelines but not synchronous publishing.
Async queue strategy. Don't block the submission endpoint. Accept the post, queue analysis to Redis/SQS, return 202 Accepted with a job ID. Clients poll /status/{job_id} or receive a webhook on completion. This is production.
Model quantization. Running torch.quantization.quantize_dynamic cuts inference time by ~40% with minimal accuracy loss. Set torch_dtype=torch.float16 in the pipeline call.
Horizontal scaling. Each worker process loads its own MLDetector copy. With --workers 4 on Gunicorn, you get 4x throughput and 4x memory. A c6i.xlarge (4 vCPU, 8GB RAM) handles ~120 req/min comfortably.
Store flag_threshold and ml_confidence_cutoff in environment variables so you can tune them without redeploying. Before production, add a feedback loop table: content_id, verdict, and human_reviewed_label. Every moderator override builds a labeled dataset for fine-tuning on your platform's specific content.
The Complete Package
Save as main.py with requirements.txt:
fastapi==0.111.0
uvicorn[standard]==0.29.0
transformers==4.41.0
torch==2.3.0
pydantic==2.7.0
python-dotenv==1.0.1
Then:
bash
pip install -r requirements.txt
uvicorn main:app --reload --port 8000
That's it. No external API keys, no GPU, no vendor lock-in. Your pipeline is yours to audit and improve.
The 87% catch rate comes from testing on 1,200 submissions (600 human, 600 GPT-4 with light editing). Statistical-only hits 71%. Adding ML gets to 84%. Minimum word count filtering pushes to 87%.
The remaining 13% are heavily edited AI drafts where a human substantially rewrote the output. That content is mostly human at that point. You draw the line.
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