Most people are aware of deepfakes, yet their ability to distinguish them from reality is barely better than a coin flip.

KEY FINDINGS

The detection deficit

When it comes to conceptual familiarity, the UK leads the pack. A significant 74% of UK adults report being familiar with the term "deepfake," which is the highest among the analysed markets, outperforming Brazil (67%) and the US (63%). However, this high awareness does not necessarily stem from an overwhelming volume of exposure; mirroring the US, only around 60% of British respondents report actually encountering deepfakes online, which is notably lower than the 80% encounter rate reported in Brazil.

Are you familiar with the term ‘deepfake’?

In the UK and Brazil, younger users are more aware of deepfakes.

In the UK and Brazil, younger users are more aware of deepfakes. The US is an exception, younger American users do not show higher awareness of deepfakes compared to older demographic.

“There's a fascinating paradox at play in the UK: Britons boast the highest familiarity with the term 'deepfake' at 74%, yet their actual detection accuracy remains barely above a coin toss. This tells us that conceptual awareness alone is fundamentally insufficient. The problem here is that theoretical knowledge without practical recognition creates a false sense of security. If people know what a deepfake is but lack the visual tools to spot one in the wild, they are just as vulnerable to fraud as those who have never heard the term. They might mistakenly assume that because they know the threat exists, they will naturally spot it when it appears on their screen.

This research shows that as AI-generated content becomes indistinguishable from reality – and we're already there – the human eye alone is no longer a reliable line of defence. "Consumer awareness and education remain a critical first step, giving people the instinct to question rather than trust by default."

When respondents were tested on their ability to distinguish between real and AI-generated visuals, performance across all markets was exceptionally low. On a scale from -1 to 1, where:

• 1 represents perfect accuracy
• 0 represents random guessing (a coin flip)
• -1 represents consistently incorrect answers

British respondents achieved a mean detection score of 0.07, matching the US and slightly behind Brazil's 0.08. A score of 0.07 indicates that, while participants performed slightly better than chance, the difference is minimal. In practical terms, these scores show that the average person is almost guessing when attempting to identify deepfakes, performing only a tiny fraction better than a coin flip.

The UK respondents' distribution of results highlights this struggle:

UK respondents are generally less overconfident than their peers; 44% of users in the UK are confident in their ability to identify deepfakes, compared to around half in the US and Brazil. Like in the US, higher confidence in the UK generally correlates with higher accuracy.

However, the UK exhibits a highly concerning behavioural flaw: 22% of UK respondents admit they do not try to verify suspicious content they see online, which is the highest rate of non-verification across all surveyed markets (compared to just 8% in Brazil).

Across the markets, approximately 7% of users are classified as high-risk:

• Demonstrate low detection accuracy
• Express high confidence in their abilities
• Rarely or never verify suspicious content

The high-risk segment is generally less common among the oldest age group in the UK and Brazil markets, with the US being the exception, meaning older Americans are just as likely to fall into this category. Respondents with a university education are less likely to fall into the "high-risk" category of users who are overly confident but inaccurate.

Approximately 7% of users across markets are classified as high-risk 

Our research reveals what may be the most dangerous dynamic in the deepfake era: overconfidence.

"Although 44% of UK respondents believe they can reliably spot manipulated media, confidence alone is no guarantee of accuracy, a significant share of self-described confident detectors still perform at or below chance level. This confidence-competence gap creates a false sense of security that fraudsters and bad actors are primed to exploit. When people believe they can't be fooled, they stop looking for the signs – and that's precisely when they're most vulnerable, whether to a synthetic identity used in financial fraud or a fabricated video designed to manipulate trust.

Most concerning is the roughly 7% of users across markets who fall into what we classify as the 'high-risk' segment – people who perform poorly at detection, are highly confident they'd catch a fake, and rarely bother to verify suspicious content. This group represents the perfect target for AI-driven fraud and manipulation.

For businesses, the implication is clear: any organisation that still relies on manual review processes or customer self-attestation is inheriting this vulnerability directly."

KEY FINDINGS

User behaviours and concerns

While creating AI visuals is widespread, UK respondents are the least likely to participate in this trend. Only 38% of UK respondents have created images or videos with artificial intelligence (11% multiple times, 27% rarely). This makes them significantly less active in AI generation than Americans (49%) and Brazilians (59%).

Importantly, the research indicates that having experience creating AI visuals provides only a limited advantage in accurately identifying fake visuals.

"The reality for businesses is that relying solely on manual review processes or customer self-attestation introduces significant vulnerabilities, especially as threats become more sophisticated. While automation and technology-led verification should be the default to ensure scale, consistency, and speed, the focus should be on strategically integrating human judgment only at critical decision points – where depth, experience, and intuition improve outcomes."

When attempting to identify fakes, UK users largely rely on the same outdated visual cues as people in other countries.

The most common indicators cited by UK respondents include:

• Unnatural-looking skin (60%)
• Unnatural movement or expressions in videos (59%)
• Oddities in appearance such as hair or teeth (54%)
• Strange or mismatched background details (48%)

While these cues may have been useful in earlier stages of deepfake development, advances in AI have made them increasingly unreliable. Modern deepfakes are capable of replicating these details with high accuracy, reducing the effectiveness of these strategies.

Additionally, the research shows that checking content more frequently does not necessarily improve accuracy, suggesting that users lack a structured or effective approach to verification.

Regardless of respondents’ age or educational background, all groups among Britons are concerned about the real-world impact of deepfakes. In the UK, the leading fears are:

The UK mirrors Brazil in its deep scepticism towards digital platforms. Unlike the United States, the majority of UK respondents have strong doubts about social media platforms' ability to identify AI-generated content. This creates a potential mismatch between perceived and actual protection. While concern is high, reliance on platforms may reduce individual vigilance.

When 81% of Britons say they're concerned about deepfake-driven personal fraud, that's not a hypothetical fear, it reflects a threat that's already materialising.

"We're seeing synthetic identities used to open fraudulent accounts and authorise transactions, and deepfake videos deployed to bypass basic verification checks. Online businesses sit squarely in the crosshairs because they are where identity, money, and trust converge. Every customer onboarding flow, every account recovery process, every high-value transaction is now a potential attack surface for AI-generated fraud.

What makes this particularly urgent for the UK market is the combination of high concern and a strong lack of trust in platforms' ability to manage AI-generated content. For financial institutions and tech companies, the answer isn't to merely reassure customers – it's to earn that trust through action. That means deploying AI-powered biometric authentication that can verify a real person in real time, detect synthetic media at the point of interaction, and do so without relying on the customer to spot the fake themselves. The deepfake arms race is an AI problem, and it requires an AI solution."

CONCLUSION

The path forward

Deepfakes are getting better

In the age of generative AI, the most uncomfortable truth this research reveals is not that deepfakes are getting better – it's that human detection was never a sole defence. UK consumers present a distinct profile:

The most effective defence is one that keeps humans in the loop, empowered by AI systems that detect what the eye cannot.

The gap between what people believe they can detect and what they actually can is not a knowledge problem that awareness campaigns alone will fix.

It is a structural vulnerability in any system that places the burden of verification solely on the human eye.

For businesses operating in the UK market, the implications are immediate and material. Fraud losses tied to synthetic identities already represent billions of pounds annually, and the tools to create convincing fakes are now accessible to anyone with a browser. Meanwhile, the roughly 7% of users who combine poor detection ability with high confidence and low verification habits represent an ever-present soft target that bad actors will continue to exploit.

Methodology overview

The research was conducted by Kantar in February 2026 using an online access panel.

Total sample: 3,000 respondents

• United States: 1,000
• United Kingdom: 1,000
• Brazil: 1,000

Participants were aged 18 to 64, with quotas applied to ensure nationally representative samples in each country based on age, gender, and region. The survey took approximately 9 minutes to complete and included both demographic questions and practical evaluation tasks.

Respondents were asked to assess 16 visuals:

• 8 real
• 8 AI-generated or manipulated

These included:

• Fully AI-generated images
• AI-generated videos
• Faceswap content

To reduce bias, all visuals were presented in randomised order. Participants completed both individual evaluations and direct comparisons between real and fake content. Detection accuracy was calculated using a scoring index that benchmarks performance against a defined chance-level baseline.

About Veriff

Veriff is a global AI-native identity platform that helps organisations build trust online. Leading companies across financial services, marketplaces, mobility, gig economy, and other digital sectors rely on Veriff’s technology to stay compliant, prevent fraud, protect users, and scale globally.

Veriff’s trust infrastructure supports the full customer journey, from verification to ongoing authentication and fraud prevention, with the least friction for honest people. Built for global scale, Veriff helps businesses expand across borders without the complexity of managing identity verification, compliance, and fraud in multiple markets – creating a single source of truth for trusted identities.