How to Detect AI-Generated Videos: 10 Signs a Video Is Fake (Business & Enterprise Guide)

AI-Generated Video vs. Deepfakes: What's the Difference

The two terms get used interchangeably, but they describe different attack vectors with different risk profiles. Knowing which one you're looking at changes how you investigate it.

10 Forensic Signals to Identify AI Video

No single artifact proves a video is fake. Compression from H.264/H.265 encoding, sensor noise, and poor lighting can all produce glitches that look suspicious but mean nothing. Treat these ten signals as a checklist — the strength is in how many converge on the same clip, not any one of them in isolation.

1. Facial Micro-Expressions and Skin Texture

Generative models tend to over-smooth skin, erasing pores, fine wrinkles, and micro-capillaries in the process. Scrub frame-by-frame and watch the earlobes, teeth alignment, and facial contours — geometry shifts there are a stronger tell than smoothness alone.

2. Phoneme-to-Viseme Mismatch (Lip Sync)

Voice cloning has gotten precise, but mapping phonemes to mouth shapes still breaks down on bilabial consonants — B, P, M — and complex dental sounds. Watch for a timing gap between the acoustic peak and the mouth actually closing.

3. Gaze Trajectory and Pupil Reflections

Check the corneal reflections. In real footage, both eyes reflect the same light sources at matching angles. Generated video often gets this wrong — mismatched pupil highlights, or a blink cadence that reads as mechanical rather than human.

4. High-Frequency Boundary Anomalies

Fine structural detail is hard to hold steady across frames. Watch for glasses merging into cheekbones, watch dials or rings that subtly change shape between shots, and corporate logos that warp into near-illegible glyphs during movement.

5. Illumination and Shadow Inconsistency

Light in the real world obeys ray optics. In synthetic footage, the light hitting a subject's face often doesn't match the shadows falling on the background, and ambient occlusion under the chin or collar is frequently missing entirely.

6. Background Temporal Drift

Stop watching the subject and watch the room. Bookshelves, architectural lines, picture frames, and background pedestrians are where generative models lose consistency fastest — expect subtle warping or dissolving during camera pans.

7. Degraded Text and Signage

Stable typography in motion is still an unsolved problem for most generative systems. Name badges, street signs, background monitors, and product labels tend to drift into illegible characters or change spelling across consecutive frames.

8. Voice Cadence and Breath Dynamics

Cloned voices often lack the organic micro-pauses and irregular breath intake of real speech. Listen for flat emotional resonance or a faint metallic quality on higher frequencies — voice cloning artifacts show up in cadence before they show up anywhere else.

9. Temporal Ghosting and Boundary Blending

Slow footage to 0.25x and watch fast movement — a head turn, a hand gesture. Synthetic media often leaves ghosting or smearing where the foreground fails to separate cleanly from the background during rapid motion.

10. Metadata Void and Missing Provenance

No verifiable source, no original broadcast feed, no signed C2PA manifest — that absence is itself a red flag. High-impact media with no digital footprint should be escalated before it's trusted, regardless of how clean it looks.

Enterprise Verification Protocol

Visual inspection catches obvious cases. Stopping fraud at scale requires a process that doesn't depend on any one reviewer's eye.

The fourth step matters most. Never authorize a wire transfer, credential change, or sensitive release on the strength of an inbound video or voice request alone — especially in live meetings, where there's no time to scrub footage frame-by-frame in the moment.