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The Ai Shift

Deepfake Ads Using A Brands Own Face

Ad Tribe Editorial2 min read
A flat vector character holding up a mask of their own face while a copy of it walks away

Deepfake ads are no longer an emerging risk in India. Scam promotions using the faces of founders, news anchors and film stars circulate widely enough that most people have seen one, whether or not they knew it.

The structure is always the same. A trusted face, a plausible platform, an investment or giveaway claim, and a landing page that collects money or details. The brand whose face was used finds out from customers.

Why Deepfake Ads Damage The Brand, Not The Forger

Because the audience has no way to sort them. A viewer who loses money to a scam using a bank executive's likeness does not conclude that impersonation is easy; they conclude that something about the brand was careless.

Trust is also asymmetric. It takes years to build the familiarity that makes a face persuasive and a fortnight of scam clips to convert it into a liability.

What A Brand Can Actually Do

Monitor for its own executives and ambassadors by name, not just for its logo, since the logo often does not appear. Establish the reporting route with each platform before it is needed, because doing it during an incident costs days.

And publish a single, permanent page stating the channels the brand advertises on and the things it will never ask for. That page is the fastest thing to point a worried customer at, and it costs nothing to maintain.

The brand did not make the ad, did not approve the ad and cannot take it down. It still owns the apology.

The Contractual Piece People Forget

Ambassador agreements should say who acts when the celebrity's likeness is misused in a scam involving the brand. In most existing contracts nobody does, so the two sides spend the first week deciding whose lawyer writes the notice.

The drill is not complicated. It is simply one nobody had to run before, and the brands that will handle it well are the ones that write it down in a quiet month rather than a loud one.

Related: accountability when work goes wrong in public.

More in The AI Shift.

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