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Learn how to spot a video created by artificial intelligence using concrete clues (text, accounts, visual details, verifications) to limit the risks of deepfakes and disinformation.
On TikTok, Instagram, YouTube, and X, generators like Sora, Veo, Runway, and Grok make synthetic scenes accessible and highly believable. A reliable method relies on a short and repeatable routine: read the context, inspect the image, and then validate with external checks. This layered approach is also the one recommended in businesses for... cyber securitybecause a single signal is never sufficient.
Contextual clues to identify a video created by artificial intelligence before watching it
Even before playback begins, several peripheral signals can already identify a video created by artificial intelligence. These clues require neither specialized tools nor editing skills, just a habit: looking at what surrounds the video, not just the scene itself. A typical case, observed in marketing teams, involves viral "product ads" that appear to be filmed in a high-end studio, especially since the account is recent and lacks a consistent history. When the concept seems too perfect, the first step is to check if the context holds up.
The presence of a watermark is a quick starting point. Some solutions apply a discreet signature, often in a corner, sometimes visible only for a few frames. Cropping or blurring can attempt to hide this watermark, but a freeze-frame is sometimes enough to make it reappear. This check should be systematic whenever a scene is circulating without a clear source.
Next, the description and hashtags often provide direct clues. Creators in good faith frequently mention the generation process, either through explicit tags or the tool's name. On mobile, this information is sometimes truncated: opening "see more" prevents missing useful details. To understand how the models work and what they actually produce, a reference resource is How does generative AI work?, which helps to interpret typical boundaries (temporelle coherence, fine details, etc.).
Official platform labels are a third lever. Since recent European regulations, mentions of synthetic content may appear. However, these labels remain incomplete: some content modified in third-party software loses its metadata, and web/mobile versions do not always display the same information. Therefore, the absence of a label does not absolve a video of its content.
The account's history is a very robust indicator. An account that publishes at an "industrial" pace, with a consistent aesthetic and consistently spectacular scenes, warrants closer examination. Scrolling back through the feed sometimes reveals older artifacts, typical of previous generation models. For organizations that want to industrialize this type of control (moderation, communication, HR), DualMedia's product and technical expertise often serves as a methodological foundation, as detailed in DualMedia digital expertiseThe key reflex: treat the context as technical evidence, not as a decor.
Reliable visual details to spot an AI-generated video on screen
Once the context is verified, image analysis can identify AI-generated videos with surprising accuracy, provided you know where to look. The models are progressing rapidly, but they still have structural weaknesses: fine anatomy, consistency across multiple frames, text in the environment, and adherence to physical laws. A simple practice is to view the video twice: once in normal playback to grasp the narrative, and then a second time in slow motion to detect minor inconsistencies. This double pass significantly reduces errors in judgment.
Hands remain a primary indicator. It's not just a matter of counting fingers, but of observing the joints: a misaligned thumb, phalanges that "slip," a ring that changes shape in motion. Anomalies often appear during rapid gestures (greeting, grasping an object, passing a hand in front of the face). In an internal demonstration at a customer service department, a "product unboxing" scene seemed perfect... until the moment the hand slightly passed through the box in two shots, invisible at normal speed.
The face provides a second set of signals. The eyes may lack micro-reflections or exhibit fluctuating asymmetry. Infrequent, overly regular, or asynchronous blinking are common markers. The mouth sometimes reveals approximate lip synchronization: lips positioned ahead, teeth in a band, and tongue absent. When audio is present, the discrepancy between phonemes and movement becomes a quick criterion to test.
On-screen text is a third pitfall for generators. Signs, panels, labels, phone interfaces: the algorithm produces elements resembling letters, but without stable linguistic logic. A common example on social networks: a very realistic street scene, with a shop window in the background whose name subtly changes between two shots. In this respect, understanding the ecosystem of tools also helps to anticipate expected artifacts; useful monitoring can be found in best AI tools 2025because each generator has its "signature flaws".
Finally, physics remains a harsh judge. Inconsistent shadows, floating objects, reflections that don't match the scene, smoke with improbable movement: these details don't stand up well to close scrutiny. To help structure this check, here's a practical checklist to apply in less than a minute:
- Pause on a frame with hands visible and check articulation and accessories.
- Observe the eyes (reflections, symmetry) and the frequency of blinking.
- Read the background text and identify the "almost legible" characters.
- Control shadows and reflections (mirrors, shop windows, shiny surfaces).
- When comparing two distant moments: clothing, jewelry, hairstyle, logos must remain consistent.
What makes the difference is the temporal consistency: a real scene supports repetition, whereas a synthesis often cracks on a few critical frames.
To illustrate the points of interest (hands, faces, text), a targeted video search can help:
To complement this with a more orientée verification and fact-checking approach, this second search is relevant:
Technical checks and best practices for methodically identifying AI-generated videos
Even if visual clues leave room for doubt, external verification often allows us to identify a video created by artificial intelligence without speculation. The goal is to transform an intuition into a body of evidence. In a professional context, this phase is comparable to a diagnosis: we cross-reference, trace, and then decide (share, report, archive). A useful analogy is to imagine the case of an SME that receives a "shocking" video on X involving a manager: before responding publicly, the communications team must validate its authenticity in less than an hour.
Reverse image search is the first tool. Capturing a clear frame (frontal view, distinctive features) and then submitting it to a search engine allows you to find earlier versions, sometimes accompanied by a watermark or explicit mention. To choose the most suitable search tool depending on the country and indexing system, a useful reference is the world's top 10 search engines in 2024In practice, using multiple search engines increases the chances of finding the original source.
The second check is on the current context. A video claiming to show a real public event almost always leaves traces: articles, photos, live feeds from other angles, local publications. The complete absence of verification is a red flag. A good reflex is to list what should exist if the scene were real (witnesses, location, time, weather, police presence), then look for verifiable evidence.
The third check concerns the video's structure itself. Many generators impose standard durations (very short clips) or produce sequences with simple actions. A 6- to 10-second video, without cuts, with very smooth camera movement and no ambient sound, warrants a more thorough check. To illustrate this point, the table below offers a quick overview:
| Signal observed | What this suggests | Recommended verification |
|---|---|---|
| A very short and "too perfect" video | Generation via consumer tool or free version | Search for longer versions, check the account and reposts |
| Inconsistent background text | Synthesis artifact or compositing | Freeze frame, zoom, frame-by-frame comparison |
| Inconsistent shadows/reflections | Failure to comply with physical constraints | Identify the light source and verify consistency across multiple planes |
| Recent account with high cadence | Automation, content farm | Auditing the feed, identifying series, searching for the same content elsewhere |
A fourth approach involves observing the processing chain. Many videos are downloaded, recompressed, and re-uploaded, which obscures the trail. To secure evidence collection (maintaining a stable version, preventing deletion), practical methods exist, including Easy ways to download videos on Android, iPhone and PCIn business, archiving the source and URL, with horodatage, simplifies exchanges with a platform or a legal department.
Finally, it remains useful to understand the tool environment: generators, editing, reposts, bots. DualMedia specifically supports organizations that need to reconcile innovation and trust, whether for mobile products, web platforms, or content flows; the underlying trends are detailed in future trends in AI in mobile application developmentWhen verification becomes a recurring need (brand, media, e-commerce), structuring a procedure and training the teams offers a clear advantage: the decision is based on cumulative signals, not on an impression.
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