AI Image Detector

The AI Image Detector analyzes an image to estimate whether it was likely generated or heavily edited by AI. You upload an image and get a result with supporting details. Useful for verifying images before publication or for learning how synthetic images can look.

The AI Image Detector applies computer vision models trained to distinguish patterns common in camera-captured photos from patterns typical of generative models (diffusion, GANs, or heavy neural editing). Outputs are probabilistic scores with supporting cues—not absolute truth. The system may examine texture regularities, frequency artifacts, edge statistics, and color distributions; newer generators evolve quickly, so detectors are always a moving target relative to the newest models. Some pipelines flag localized anomalies that suggest inpainting or splicing; fully synthetic scenes may differ from photos with small AI edits. Metadata such as EXIF may be shown when extractable, but absence or tampering does not prove either authenticity or fakery. SynthQuery positions image detection as a triage step alongside human review, reverse image search, and provenance tools like Content Credentials when available. It is educational and operational for newsrooms and creators— not a substitute for forensic lab analysis in legal proceedings.

Newsrooms and fact-checkers screen user-submitted photos before broadcast or social syndication. E-commerce marketplaces spot suspicious product images when vendor behavior is risky. Brand safety teams review campaign assets for undisclosed synthetic elements where contracts require disclosure. Educators teach media literacy by comparing detector outputs with real examples of AI art. Corporate communications teams verify executive headshots before annual reports when deepfake risk is high. Game and film studios may use signals internally when reviewing contractor deliverables—never as sole grounds for legal action. Researchers studying generative media should document model versions and avoid overclaiming detector accuracy in papers. Every use case requires human judgment and ethical policies around consent and privacy.

Start from the highest-quality original you can access—prefer native camera files or uncompressed exports over recompressed memes. Avoid upscaling before upload; synthetic upscalers add artifacts that confuse classifiers. Crop to the subject area when the UI allows, but keep lighting context if authenticity matters. Upload the file, read the score as a triage flag, then run parallel checks: reverse image search, metadata review, and source interviews when feasible. For journalism, follow your organization’s verification checklist (often including geolocation and chronology). If the image will be published, disclose AI generation or editing when your policy or platform requires it. Do not publicly accuse individuals based solely on detector scores—false positives harm real photographers. Store the report alongside the asset for auditability. If stakes are legal or criminal, escalate to forensic specialists with chain-of-custody procedures. Retest periodically; model updates may change scores on the same file.

Detectors lag cutting-edge generators. Heavy filters, screenshots, and compression can distort scores. Legitimate CGI and photography can be misclassified. Never use scores alone for employment, academic, or legal sanctions. Combine with provenance, witness evidence, and editorial policy. Respect privacy and consent when analyzing personal images.