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Concept

Deepfake

The BasicsUpdated on 16 August 2026we are coded

When your eyes are no longer a witness: video and voice manufactured by a machine, of a person who never made them.

Checked on16 August 2026
In short: a deepfake (from deep learning and fake; the word dates from 2017) is synthetic video, image or voice in which a real person does or says something that never happened. It is made with generative models, ever cheaper and ever more convincing. The answer is not “trust nothing” but a new habit: what matters gets confirmed through a second channel.

Cinema has had doubles for a century: the stuntman falls off the roof, the audience sees the star. Hundreds of people and months of work to fool the eye for two seconds. A deepfake is the same trick, except the double is a model, the set is a graphics card, and the whole crew fits into one line of text. What used to be an industry became a button.

The danger is not mainly in big politics, however much that gets discussed. It is in the everyday: the voice of “your daughter” urgently asking for money over the phone. “The boss” ordering a transfer over a video call. The scams are not new - what is new is how convincing the mask is.

The question is no longer “does it look real” but “has it been confirmed through a second channel”.

What actually helps

Three habits. First: urgency plus money equals pause - call back yourself, on the number you know, not the one calling you. Second: read the context, not the pixels - who published the clip, does anyone else have it, what does the source who should know say. Third: there are technical markers too - watermarks and content-provenance standards mark what is generated, even if they are not everywhere yet.

Our read: the panic of “nothing can be trusted anymore” is exactly as wrong as naivety. Counterfeit banknotes have always existed - which is why banknotes carry protections and people have the habit of holding them up to the light. The same habit, carried over to video and voice, works here too.

The visual is generated code art. No third-party images.
Official primary sources
→NIST: Reducing Risks Posed by Synthetic Content (AI 100-4)