AI Video Detector

Methodology

AI Video Detection Methodology

Understand how AI Video Detector analyzes video frames, motion patterns, metadata, and compression signals to produce a likelihood score and confidence label.

How the scanner analyzes video

Frame extraction

The scanner extracts key frames from the video at regular intervals. The number of frames depends on video length and your plan. More frames provide more signal for temporal analysis.

Visual signal analysis

Each extracted frame is analyzed for visual artifacts common in AI-generated content: unnatural skin texture, inconsistent lighting, blurry edges, repetitive patterns, and compression anomalies that differ from standard camera output.

Temporal consistency

The scanner compares consecutive frames to check whether motion follows natural physics. AI-generated video often shows subtle temporal glitches — objects that morph, shadows that shift direction, or textures that change between frames without a lighting change.

Motion physics

Real-world motion follows predictable physics: gravity, inertia, momentum. AI-generated video sometimes violates these rules in subtle ways — hair that moves unnaturally, water that flows incorrectly, or objects that accelerate inconsistently.

Facial landmark analysis

For videos containing faces, the scanner tracks facial landmarks (eyes, nose, mouth, jawline) across frames. Deepfake face swaps often show inconsistencies in blink rate, eye movement, lip-sync timing, and skin texture at the face boundary.

Compression artifact analysis

AI generation pipelines leave specific compression patterns that differ from standard camera encoding. The scanner examines frequency-domain artifacts to detect these patterns.

Metadata checks

When available, the scanner checks video metadata (encoding parameters, creation date, device info) for inconsistencies. AI-generated video often lacks the metadata that real camera recordings carry.

Score aggregation

All signals are combined into a single 0–100 AI likelihood score. No single signal is conclusive — the score reflects the combined weight of multiple weak signals. A confidence label (Low / Medium / High) indicates how much usable evidence was found.

When results are more and less reliable

Results are more reliable when

  • The video is the original file (not a screen recording or repost)
  • The video is at least 5 seconds long
  • The video has not been heavily compressed or re-encoded
  • The video contains faces (for face-swap detection)
  • The video has good lighting and resolution

Results are less reliable when

  • The video has been compressed by messaging apps or social platforms
  • The video is very short (under 3 seconds)
  • The video uses heavy filters, beauty effects, or color grading
  • The video is a screen recording of another video
  • The video is animated, a slideshow, or gaming footage
  • Only part of the video is AI-generated (e.g., background only)

What this tool does NOT do

  • It does not prove a video is real or fake. Results are probabilistic signals for review.
  • It does not identify the specific AI tool used to create a video.
  • It does not analyze audio separately from video frames.
  • It does not work well on non-face content (landscapes, objects) for deepfake detection.
  • It is not a forensic, legal, or law enforcement service.
  • It does not replace human judgment. Always combine scan results with source verification and context.

Why results are probabilities, not proof

Every AI detection method has limitations. Generation models evolve, and detectors must keep up. A score of 78/100 means the scanner found strong AI-generation signals — but it does not guarantee the video is AI-generated. Similarly, a score of 12/100 means few signals were found — but it does not prove the video is real.

The strongest verification workflows combine multiple approaches: automated scanning as a fast first pass, manual review on anything flagged, and source cross-referencing throughout. For high-stakes content, always escalate to a human expert.

For a detailed breakdown of accuracy limits, see Accuracy and Limitations. For a sample of what results look like, see Sample Report.