AI image detector: what it can tell you, and what it cannot

## The Probability Game When you use an [AI image detector](/), you aren't getting a "Yes/No" from a divine oracle. You are getting a **probability score**. "98% AI" means the model found statistical patterns (noise distribution, compression artifacts, semantic inconsistencies) that strongly correlate with training data from generators like Midjourney or DALL-E 3. ### Where detectors struggle 1. **Heavy Compreesion**: Social media platforms strip metadata and re-compress images, destroying the subtle "fingerprints" detectors look for. 2. **Hybrid Images**: A real photo with a small AI-generated cat added to it. The overall image stats might look real, masking the edit. 3. **Adversarial Noise**: Sophisticated actors add invisible noise layers specifically designed to fool detectors. ## Why it matters Over-reliance on automation is dangerous. False positives can accuse innocent artists; false negatives can let fraud slip through. ## Quick checks * **Don't stop at the score**: Use the score as a filter. If it's high, investigate. If it's low but the image looks suspicious, investigate. * **Look for semantic errors**: Hands, text, physics. AI is getting better, but logic errors persist. ## What to do next For critical verification: 1. Start with our [AI image detector](/). 2. If the result is ambiguous or the stakes are high, use our **Human Certificate** service to get an expert review.