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AI Content Detection

AI Content Detection

DEJAN-LM is an AI content detection model trained on 20 million sentences, using a combined deep learning and heuristic approach to identify advanced AI text.

As models advance, AI content detection tools are struggling to keep up. Text generated by the latest Gemini, GPT and Claude models is fooling even the best of them.

We’ve decided to bring AI content detection back in-house in order to keep up. Each time a new model comes out the classifier needs a fine-tune on that model’s output.

Our base model, DEJAN-LM was pre-trained on a 10,000,000 sentence dataset using masked language modelling (MLM) on top quality content from websites with excellent editorial practices. DEJAN-LM is a web article expert.

The model was fine-tuned for AI content detection on a 20,000,000 sentence dataset, 50% original human content, 50% AI paraphrase or derivative content.

A training loss graph shows the loss value sharply dropping from 0.7 to 0.1 over several thousand steps.

Test Results

GPT-4

An article about Grok with most sentences highlighted in red, indicating a ninety-seven point eight percent AI likelihood score.

GPT-4.5

An AI content detection report displays a block of text highlighted mostly in red, indicating an AI likelihood score of 87.3%.

GPT-4o-mini

A text passage about Elon Musk's xAI with sentences highlighted in red and green, indicating a 64.3 percent AI likelihood.

GPT-4o

A block of text about Grok with sentences highlighted in red, pink, and green showing an AI likelihood of 41.3 percent.

GPT-o3

A block of text with sentences highlighted in green and red, showing an AI likelihood score of 33.1 percent.

GPT-o4-mini

A paragraph of text highlighted in green above a score indicating a low AI likelihood of twenty point seven percent.

Manual Algorithm & Heuristics

It’s clear that OpenAI’s latest model flies under the radar and avoids deep-learning based detection so we went old school. The 20,000,000 sentence dataset was processed to define top 1000 words for each class sorted by dataset count. We then normalise their values allowing for non-discriminating words to self-eliminate.

A bubble chart comparing original human content in green with AI-generated content in pink across a diagonal grid.

The two lists of top words and their weights were used in a simple ranking algorithm to help our deep learning model where it struggles.

Four mathematical formulas show the calculation steps for word frequency, normalized frequency, indicator score, and normalized indicator score.

As a result the classification confidence for the elusive GPT-o4-mini went from mere 20.7% all the way to 68.1% which puts it in the “Yes, it’s AI generated!” category.

  • Model AI Likelihood: 20.8%
  • Heuristic AI Likelihood: 47.3%
  • Combined AI Likelihood: 68.1%

Dan Petrovic · Apr 17, 22:52