Deepfake videos have reached a level of realism that makes them hard for the public to distinguish from genuine footage. CNET outlines concise, non technical checks anyone can use, grouping signs into visual cues, audio signals, playback artifacts, and provenance gaps. For businesses, routine verification is an essential layer of deepfake fraud protection and misinformation prevention.

Why realistic deepfakes matter

A deepfake is AI generated or AI altered video that substitutes, modifies, or fabricates a persons face, voice, or actions. Advances in generative models and easy to use tooling have lowered the barrier to create lifelike fakes. Video remains one of the most persuasive formats for audiences and decision makers, which makes AI generated video verification and content authenticity verification critical for protecting brand trust and customer safety.

Key visual cues to check

• Blinking and eye behavior: Look for uneven or unnatural blinking and eyes slightly out of sync with head motion.
• Lighting and reflections: Check for inconsistent lighting on a face or mismatched reflections in glasses, windows, or water.
• Facial edges and hair: Notice warped facial edges, hair that clips through objects, or inconsistent hairlines.
• Background transitions: Watch for odd transitions where the subject meets the background or elements that shift independently.

Audio, lip sync and playback artifacts

• Cadence and intonation: AI voices can sound monotone, too smooth, or lack natural emphasis.
• Lip sync errors: Small mismatches between lip movement and syllables, especially on fast consonants, are telling.
• Frame level glitches: Flicker, frame drops, or stuttering at normal playback can point to synthesis artifacts.

Provenance and metadata checks

Missing or altered timestamps, absent camera metadata, and files without a clear source are red flags. Video authentication and provenance tracking, including solutions like content credentials and digital watermarking, help establish authenticity. When available, tools and standards such as C2PA provide useful content provenance signals for verification.

Four quick verification steps

1. Reverse image or video search: Capture a clear frame and run a reverse image search or use reverse video tools to find original source material. This is a high value step in synthetic media verification.
2. Check the posting account: Verify the accounts history, verified status, and pattern of reliable sourcing before trusting the clip.
3. Use browser extensions and detection tools: Employ reputable deepfake detection tools and browser extensions for an automated second opinion. Many detection services now combine visual and audio analysis for better results.
4. Cross check with trusted outlets and primary sources: Confirm claims with original publishers, official statements, or multiple reliable outlets to limit misinformation spread.

Operational recommendations for organizations

• Train frontline staff: Teach employees the visual checks and verification steps so suspicious content is escalated quickly.
• Deploy monitoring tools: Use automated deepfake detection tools and brand monitoring services that flag probable fakes for human review.
• Invest in provenance: Adopt content authentication standards, signed video, and verified metadata where possible to harden sources and support content authenticity verification.
• Prepare response plans: Establish a rapid communications workflow to correct misinformation and protect reputation.

Caveats and final note

Detection tools are improving but are not perfect. Some AI systems are tuned to evade common heuristics, and benign editing can resemble manipulation. Even so, a layered approach combining human verification, automated deepfake detection tools, and provenance systems reduces risk significantly. Companies that standardize quick checks and adopt video authentication practices will be better positioned to limit harm as synthetic media becomes more prevalent.

Which verification habit will your team adopt first: reverse searches, browser based detection, provenance tracking, or staff training?