This is interesting timing.

Today, ahead of the Global Anti-Scam Summit, which is being held in Washington this week, Meta has shared some insights into its evolving efforts to combat scams in its apps, including these impressive data points:

• In the last 15 months, reports about scam ads have declined by more than 50%, and so far in 2025, we’ve removed more than 134 million scam ads.
• In the first half of 2025, our teams detected and disrupted nearly 12 million accounts – across Facebook, Instagram, and WhatsApp – associated with the most adversarial and malicious scammers: criminal scam centers.
• We’re using facial recognition technology to stop criminals that abuse images of celebrities and other public figures to lure people into scams.

These are impressive numbers, right? 12 million accounts is a lot.

But then again, at Facebook’s scale, with over 3 billion users, 12 million is nothing, a tiny fraction of its total user base.

And when you also contrast these numbers against recent reports that Meta has knowingly generated around $16 billion per year from scam ads, which its system allows to run, despite it detecting questionable elements within these promotions, those figures don’t actually seem as impressive in contrast.

Those numbers stem from a Reuters investigation into Meta’s internal processes to detect and filter out potential scam ads.

According to the report, Meta “failed to identify and stop an avalanche of ads that exposed Facebook, Instagram and WhatsApp’s billions of users to fraudulent e-commerce and investment schemes, illegal online casinos, and the sale of banned medical products.”

The main flaw, Reuters’ report suggests, is that Meta’s systematic thresholds for what constitutes a scam ad are too lax, which has allowed many of these ads to be shown to users, despite Meta’s system flagging concerns.

That, in the end, has led to Meta generating billions from these scam ads. And when you also consider that around 23% of adults globally lost money to scams in 2024, with Facebook being the second most cited source of such activity (WhatsApp came in first), that does somewhat belie Meta’s topline figures that promote its evolving security systems.

To be fair, the Reuters report includes data from 2024, and Meta is saying here that it’s seen a 50% reduction in reports of scam ads over the past year. So it may well have improved since then, but the fact that Facebook is such a prominent vector for such scams doesn’t really support Meta’s claims, at least at this stage, that it’s doing more to protect users.

And the impacts of this extend to all social platforms. When someone loses money to some Facebook scam, they’re way less likely to try social media shopping options again, while they also warn their friends about potential scams, warding off more potential in-stream shoppers.

That, at least in part, may be why Western consumers are more reluctant about social shopping than those in Asian markets, where the preference seems to be to incorporate as many functions as possible into a single app.

Western consumers are more likely to keep their social and entertainment activity in certain apps, and shopping in other, trusted platforms. The prevalence of scams, then, may well be what’s restricted platforms like TikTok from making big money out of its in-stream sales push, which is why increased security, and a focus on this element is critical.

But if Facebook’s not looking to drive in-stream sales, and it can generate billions from scam ads, it’s not clear whether it’ll have the motivation to really address this element.

The numbers above suggest that it is taking more action, and it may well be improving on this front, but at this stage, the rate of scams in Meta’s apps is a problem for the broader social media industry.

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YouTube has shared some additional info on its coming likeness detection tool, which will enable creators to upload an image of their face, which YouTube will then be able to use as a reference to detect other uploads that include their likeness.

Which could include re-posts of their own content, or increasingly, AI deepfakes, giving creators the opportunity to request removal of unauthorized usage.

YouTube officially announced the option at its Made On event last month, though it’s actually been working on various identity detection measures for the past year, including visual likeness detection, but also audio detection as well, to help musicians police their IP.

But facial recognition, and detecting unauthorized use of your likeness, is the focus of this update.

YouTube’s new tool will use face scans, as well as government ID, to reference check across uploads.

As you can see in this sequence, YouTube’s likeness detection will be enabled via YouTube Studio, and will require creators to upload their government ID, along with a face scan.

The face scanning element could be somewhat controversial, given past misuse of face ID, and concerns about privacy. In this process, your face scan will be uploaded to Google’s servers for processing, and it will need to keep that face scan on file, and matched up with your government ID, for future detection.

So you will have to submit your facial recognition info to the app, which, as noted, could be a privacy concern. But there’s not really any other way to do this.

And with more and more AI fakes being made, particularly of high-profile people, this does look set to be reignited as a point of debate on this front.

Once you’ve submitted your face scan and info, YouTube will then be able to highlight when your likeness has been used across the app, with results presented in a list to review.

YouTube notes that this may well be empty for you, which is completely normal. But if YouTube’s system can find visual matches, you’ll be able to view them all here.

From there, you’ll have the option to submit a removal request if you don’t approve of this usage:

It’s a fairly straightforward system, using Google’s advancing entity recognition to pick out matching instances across the millions of videos uploaded to the app every day.

Which could also mean that it may still miss some, while there could also be false positives. Also, using AI tools, re-posters may seek to evade detection when ripping off your clips, by reimagining your original content in another form.

As such, this isn’t a 100% guaranteed way to detect and stamp out misuses of you or your ideas. But it’s a significant step, which will give creators more ways to manage such.

YouTube says that it’s been testing this tool with a small subset of creators over the past few months, and will be looking to expand it to all creators in the YouTube Partner Program (YPP) in the near future.

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