How To Spot An AI-Generated Video Before Sharing It

Sophisticated AI-generated videos are blurring the line between real and synthetic content, posing significant challenges for digital literacy. Deepfakes, content that convincingly portrays real people saying or doing something they never did, are increasingly used for misinformation, financial scams, and manipulating public opinion. To identify AI-generated footage, look for unnatural facial expressions or movements, distorted hands or backgrounds, and mismatched audio or lip-sync. Inconsistent text, odd lighting and missing metadata are also red flags. Beyond visual cues, verify content by checking platform labels, conducting reverse searches, utilizing tools, and scrutinizing the source account's history. Always pause before sharing emotionally charged or unverified clips.

You're scrolling through your feed when a clip stops you cold — a politician saying something shocking, a celebrity endorsing a product they'd likely never touch, a historical figure promoting a product, a "news" clip from a disaster that never happened.

A few years ago, spotting fake footage was easy. Now, knowing how to tell if a video is AI generated has become a basic digital literacy skill. Tools like Veo and Kling can produce a minute or more of photorealistic footage from a single text prompt, and the lines between real and synthetic video seem to keep blurring or shrinking.

An AI video is any footage generated, altered or synthesized by artificial intelligence rather than captured by a camera. That can comprise a fully synthetic clip built from a text prompt or even real footage manipulated to change what someone appears to say or do.

The range of what AI videos look like may be wide. At the more fictional end, it might be a fully invented AI "influencer" with millions of followers who has never existed as a real person. At the more dangerous end, it might look like the AI-generated robocall that mimicked President Biden's voice in January 2024 and told New Hampshire voters to skip the primary — a clip that was estimated to have targeted as many as 25,000 households before it was identified as fake.

The term “deepfake” refers to AI-generated or altered media, usually video or audio, that convincingly depicts a real person saying or doing something they never actually did. It typically relies on deep learning models trained on images or recordings of a specific person’s face and voice. A broader AI-generated video — such as a fantastical animal skit, for example — doesn't necessarily involve a real person's likeness and isn't automatically deceptive or harmful.

Synthetic video has been circulating since roughly 2017, when early face-swap tools first surfaced online, but the technology has advanced by leaps and bounds since 2024 and 2025 with the arrival of text-to-video generators capable of simulating realistic motion, lighting and physics.

An AI-made video isn't inherently untrustworthy — plenty are labeled, satirical or clearly fictional — but the same tools have also been used to spread misinformation, run financial scams, dupe several groups of people and manipulate public opinion around elections. In early 2024, the British engineering firm Arup lost approximately $25 million to a deepfake scam in which a finance worker was tricked into transferring funds after a video call with what appeared to be his company's CFO and other colleagues but were fake voices and images. That same year, 82 deepfakes were identified across 38 countries; these impersonated public figures and 30 of these nations were holding elections or having elections planned for 2024.

Learning to spot an AI-made video has become a matter of basic digital literacy and self-defense. The ability to fabricate convincing footage of real people saying things they never did does indeed enable scams, but in the bigger picture, it also erodes the baseline trust that makes sharing video meaningful and powerful in the first place. When credible-looking content can be manufactured at scale and distributed instantly, the burden shifts to every viewer to verify before they share. The signs may not always be obvious, but they are consistent enough to be learnable.

Synthetic video detection works best when you're stacking several small inconsistencies together rather than hunting for one single dead giveaway. Still, some visual and audio clues are reliable tells, especially around faces, hands, backgrounds and sound.

Here's where to look and what to look for first.

Faces are often where AI video generators still slip up. Watch for blinking that looks too fast, too slow or oddly regular; expressions that don't quite match the emotion of the scene; and skin, eyes and hair that shift subtly in texture from one frame to the next even though it's supposed to be the same person throughout the clip.