How AI Content Detector Works
Our tool uses advanced machine learning to analyze text, images, and videos — helping you quickly determine if content was likely created by AI or by a human.
Text Detection
- 1. Perplexity — Measures how "predictable" the text is. AI text often feels too smooth and consistent (low perplexity), while human writing has more surprises and variation.
- 2. Burstiness — Looks at sentence length variation. Humans write with natural rhythm (short + long sentences); AI tends to be more uniform.
- 3. Pattern Recognition — Our RoBERTa-based model scans for subtle linguistic fingerprints left by large language models like GPT, Claude, or Gemini.
The tool truncates long inputs to stay within model limits — this keeps results fast and reliable.
Image Detection
- 1. Visual Artifacts — AI images often show unnatural patterns: perfect symmetry, anatomical errors (extra fingers, merged limbs), inconsistent lighting, or weird textures.
- 2. Deep Learning Classification — Our Vision Transformer model is fine-tuned to spot deepfake/AI-generated faces and scenes (trained on thousands of real vs. synthetic examples).
- 3. Outputs a confidence score — higher means more likely AI-generated (e.g., from Stable Diffusion, Midjourney, Flux, or DALL·E).
Video Detection
Videos are trickier — we use a hybrid approach:
1. Frame Sampling
The Tool extracts 5–15 representative frames from the video.
2. Image Analysis
Each frame is checked using our image detector for AI artifacts.
3. Averaging
The average confidence scores across frames — if most look synthetic, the video is flagged as likely AI-generated.
This method catches many deepfakes and AI videos (e.g., from Runway, Kling, or Synthesia) effectively.