AI Video Detector: How to Check AI-Generated Video

Most synthetic-video classifiers use machine-learning models trained on examples of camera-captured and AI-generated video. They may inspect spatial textures, temporal consistency, motion patterns, frequency-domain signals, codec behavior, faces, lighting, or correlations that humans do not see directly. A video detection model outputs a probability or score, not a mathematical proof of origin.

The weakness is distribution shift. When a new generator such as LTX-2.5, Wan 3.0, or Seedance 2.5 changes the visual statistics of AI-generated video, an older detector may not recognize it. Conversely, heavily compressed human footage can look synthetic to a classifier. Detection quality must therefore be validated against current generators and real-world editing pipelines.

A provenance system asks where the file came from rather than only how it looks. Standards such as C2PA can attach signed information about capture, editing, and generation history. When trustworthy provenance is present and intact, it can be more informative than a classifier because it records a chain of origin rather than inferring one from pixels.

Provenance is not perfect. Metadata can be stripped, some platforms do not preserve it, and older cameras or generators may never add it. That is why AI-generated video verification should combine provenance with source history, upload timing, creator disclosure, and independent video detection methods.

Visual review is still useful when done carefully. Inspect hands interacting with objects, reflections, contact shadows, repeated patterns, text, jewelry, small logos, background faces, object permanence, and transitions across frames. AI-generated video can maintain a beautiful global style while small details morph or violate physical continuity.

Audio deserves separate review. Listen for room acoustics that do not match the camera position, speech that is too clean for the environment, inconsistent breath and mouth movement, repeated ambience, or sound effects that arrive slightly before or after the visible action. These clues are not definitive, but they can tell you where to focus a more formal detector or forensic check.

Start by preserving the original file and recording where it was obtained. Check metadata and provenance, then search for earlier copies or source uploads. Extract representative frames and run reverse-image searches. Only after those steps should you compare one or more detector results, because the contextual evidence can explain why a classifier behaves strangely.

For a detector you plan to use repeatedly, build a local benchmark. Generate controlled AI-generated video samples with known models such as LTX and Wan, capture real phone footage, then apply the same compression and social-media processing to both sets. A video detection service is useful only if it performs acceptably on the kind of files your team actually receives.