Deepfakes: Myths, Facts, and What Really Matters

The biggest misconception about deepfakes? That you’ll always be able to spot them. For Cybersecurity Awareness Month, we’re breaking down the most common myths, the facts behind them, and why detecting AI-generated impersonations is harder than most people think.

Think you can tell real from fake? Test your instincts in our interactive challenge: Can You Spot the Deepfake?

Why we fall for deepfakes

Confidence in detecting deepfakes is worryingly low. A Business.com study found that 32% of business leaders have no confidence that their employees could recognize a deepfake fraud attempt against their organization. This isn’t just a lack of training or awareness; it reflects how sophisticated these AI-generated impersonations have become.

As deepfake attacks evolve in speed, scale, and realism, relying on instinct or human judgment alone isn’t enough. The stakes are high. AI-enabled fraud is projected to cause $40 billion in global financial losses by 2027 (Group-IB). Businesses, governments, and individuals alike need to rethink what “trust” looks like in the age of synthetic media.

So where does that leave us? Between overconfidence and uncertainty, two factors that make deepfakes so powerful. Misconceptions about how they’re made, where they appear, and how easily they can be detected only add to the problem.

Deepfake myths vs. facts

The truth about deepfakes is often buried beneath hype and misconception. Understanding what’s real and what isn’t is the first step to defending against them.

Myth 1: Humans can reliably spot deepfakes by eye

Fact: We can’t. Humans routinely overestimate their ability to detect AI-generated content.

Machine models consistently outperform humans in distinguishing real from fake audiovisual content. In controlled studies published on arXiv, artificial intelligence detectors proved significantly more accurate than human observers. Detection tools and automated analysis are now essential, because human instinct alone is no longer enough.

Myth 2: Detection always keeps pace with deepfake generation

Fact: The “arms race” is real, and detection often lags behind creation.

As new generative models emerge, such as Generative Adversarial Networks (GANs) and diffusion systems, detection methods must be retrained to recognize new artifacts. Research shows generation techniques frequently outpace defenses, creating periods where attacks go undetected (arXiv). 

Myth 3: Deepfakes are only about video or images

Fact: Audio and multimodal fakes are growing faster than visual ones.

Voice cloning is now one of the top attack vectors in AI-enabled fraud, and hybrid multimodal fakes combining manipulated video, audio, and text are increasingly common, according to DeepStrike. These attacks bypass traditional authentication because they sound authentic even when they're not.

Myth 4 Deepfakes remain rare or niche

Fact: They’re scaling fast.

According to Security.org, deepfake fraud incidents grew 10× between 2022 and 2023, with cases spanning finance, recruitment, and social engineering. And with eMarketer forecasting that generative AI is forecast to reach 51% of U.S. internet users by 2029, the tools are now in many hands. Deepfakes aren’t limited to celebrities or politics; they’re showing up in job interviews, customer service, and C-suite impersonations.

Myth 5: Big Tech platforms have strong verification that prevents impersonation

Fact: Even advanced verification processes can be easily bypassed. 

When OpenAI launched Sora 2, its new text-to-video model and sharing platform, Reality Defender researchers created convincing deepfakes of CEOs and celebrities that bypassed Sora’s multi-step “Cameo” verification checks within 24 hours. Despite live video and verbal authentication steps, the platform’s safeguards failed to detect the impersonations, proving that current verification tools are not yet equipped to handle AI-generated manipulation.

Beyond the Myths: The Reality Defender Approach

At Reality Defender, we’re building digital integrity for an era where AI-generated media evolves faster than traditional defenses can react. Our research-led approach reviews emerging generative techniques, ensuring our detection models remain resilient against new manipulation methods.

Our multimodal platform analyzes voice, video, and images in real time, identifying subtle pixel, frequency, and temporal artifacts invisible to the human eye. By continuously updating our detection models, we close the gap between creation and detection. Our goal is to deliver robust, scalable deepfake protection that strengthens organizational resilience and restores trust across the digital ecosystem.

Can You Spot the Deepfake? Take the Challenge.

Organizations are increasingly caught off-guard by realistic audio or video impersonations that slip past even advanced cybersecurity systems. We've launched the Deepfake Detection Game to raise awareness and show just how challenging detection really is. It’s a fast, interactive way to test your instincts:

• Review real and AI-generated faces and videos.
• Decide what’s real or AI-manipulated in seconds.
• See how your score compares to others on our leaderboard. 

Play the Deepfake Detection Game and see if you can spot the fakes. Then share your score, challenge your colleagues, and help spread awareness this Cybersecurity Awareness Month.