How AI Image Detectors Work: From Pixel Patterns to Probabilistic Verdicts The rise of generative models has made images indistinguishable from reality to the human eye, and that has accelerated demand for reliable tools that can identify synthetic content. An ai image detector examines images at multiple levels: low-level pixel statistics, mid-level artifacts introduced during generation, and high-level semantic inconsistencies. Low-level analysis looks for subtle irregularities in noise distribution, compression signatures, color banding, and frequency-domain anomalies. These micro-features may not be visible, but they leave measurable traces that machine learning models can learn to recognize. At the mid level, detectors probe for generation-specific artifacts produced by GANs, diffusion models, or…