AI Image Detector API

Walter scans an image for the statistical and visual patterns that AI generators and editing tools leave behind, then returns a probability that it is AI-generated or manipulated, with region-level detail. Some fakes show obvious tells like warped hands or garbled text, but modern generators are good enough that the eye alone is unreliable, so a scored, automated check is far more dependable at scale. Treat the score as a signal to review, not a final verdict.

A deepfake is a synthetic or manipulated image that makes someone appear to be or do something they did not, usually through face swaps or AI-generated portraits. The API flags deepfakes and face manipulation alongside fully AI-generated images, returning a confidence score you can threshold for your own workflow.

It detects images from the major generators, including Midjourney, DALL-E, Stable Diffusion, and Flux, plus edited and inpainted photos. We retrain on new models as they ship, so coverage keeps pace with the tools people actually use.

It returns a probability, not certainty, and like any detector it can produce false positives and false negatives, especially with heavy compression, screenshots, filters, or a generator it has not seen before. Walter returns a calibrated value between 0 and 1 instead of a hard yes or no, so you set the threshold and pair it with human review for high-stakes decisions.

The model looks for the subtle artifacts and statistical fingerprints that generation and editing leave in pixels, frequency patterns, and metadata, which differ from how a camera captures a real scene. It returns a probability and highlights the regions most likely to be synthetic, rather than relying on any single visual tell.

We retrain the detection models regularly on outputs from the latest generators. Detection-evasion is theoretically unbounded over the long term, but practical accuracy stays high when the detector keeps pace with new models, much like antivirus depends on staying current rather than on static rules.