Deepfake dangers drive evolution of market for synthetic media detection

Deepfakes are the issue of the moment. As generative AI continues to dominate headlines and gain users of all stripes, the potential for fraud grows, offering tools that make it cheap and simple to modify an existing identity, or create an entirely new one. Political figures are seeing their faked likenesses make speeches they never made, and X’s Grok AI is churning out obscene deepfakes of Taylor Swift.

In a recent article for Biometric Update, cybersecurity advocate James R. McQuiggan writes of the increasing prevalence of so-called AI slop, and explores the concept of the The Liar’s Dividend, which “describes the advantage gained by those who spread false information in an environment flooded with misinformation.” It is the enabling of plausible deniability with a call of “fake news” – and the removal of consequences for treating reality as fungible.

The challenge is complex and has massive implications for foundational social concepts like truth, evidence and accountability. In short order, it has spurred a sector of companies offering solutions, in the form of advanced detection systems for synthetic or manipulated media (profiled in the 2025 Deepfake Detection Market Report & Buyer’s Guide from Biometric Update and Goode Intelligence).

Reality Defender brings deepfake media detection to data analysis

Reality Defender is partnering with data intelligence monitoring firm Primer Technologies to build what Reality Defender CEO Ben Colman calls “the first AI-native intelligence stack that doesn’t just spot emerging threats, but actually verifies whether content comes from real humans or sophisticated AI systems.”

A post on the deepfake detection firm’s blog says the partnership will equip Primer’s Enterprise and Command products, which track patterns and risk signals across the digital landscape, with Reality Defender’s multi-model authentication layer, providing detection and verification of synthetic media across video, audio, images and text.

Colman says “the result is something unprecedented: real-time synthetic content detection embedded directly into intelligence analysis.” He says it’s a shift toward proactive defense that stems directly from the partnership uniting deepfake detection with data analytics.

Primer CEO Sean Moriarty says that, “by integrating our analysis products with Reality Defender’s synthetic media detection, we’re offering solutions that provide unprecedented insight into the authenticity and context of online content.”

“Government agencies conducting threat assessments, corporations protecting their brand reputation, security teams investigating suspicious activity – they all get the same critical advantage of knowing not just what’s happening, but whether it’s actually real.”

Reality check: deepfakes bleeding into personal interactions

The question of reality is central to the discussion about deepfakes, and generative AI overall. It sounds absurd to suggest that the fabric of reality is in danger of fraying – but increasingly, observers are pointing out just how destabilizing a world of unregulated deepfakery could look.

An opinion piece in the New York Times by columnist Zeynep Tufecki stamps out the notion that “critical thinking” will be enough of a remedy to what’s at hand.

“Video was among the last bastions of verification, exactly because it was difficult to fake,” she writes. “Now that that’s gone, the real, and increasingly the only, way to be confident of something that one did not witness is to find a reputable source and verify. Ah, what’s a reputable source, you ask? And therein lies what’s left of our society.”

Tufecki imagines a world in which deepfake videos are easy enough to make that they could throw the entire concept of liability into chaos. “Caught on camera keying the neighbor’s car? Just claim it was a deepfake. Or produce your own deepfake, showing someone else in the act. Hey, it’s your word against theirs. Or maybe it really was a deepfake. How do you disprove it?”

The solutions get some attention here, with Tufecki noting that “scientists and parts of the tech industry have come up with a few very promising frameworks – known as zero-knowledge proofs, secure enclaves, hardware authentication tokens using public key cryptography, distributed ledgers, for example.”

Likewise looking at how the risks of deepfakes are evolving, a blog post from the LSE Business Review identifies the promise in digital watermarking, cryptographic metadata and blockchain-backed provenance systems, as potential tools to help fix the growing problem.

But the overarching message from both parties is a warning.

“Unless we start taking the need seriously now before we lose what’s left of proof of authenticity and verification, governments will step right into the void,” Tufecki writes. “If the governments are not run by authoritarians already, it probably won’t take long till they are.”

The LSE is equally concerned. “Public debate around deepfakes often centres on political disinformation, especially during election season. However, this view is too narrow,” it says.  “Deepfake-related harm now reaches into public safety, health, financial systems and crisis response.”

As such, it does not, like Tufecki, dismiss the role of awareness, which it considers necessary to an effective hybrid approach.

“Understanding that deepfakes are not confined to politics, but rather embedded across everyday life, is key to fostering institutional, technical and civic responses,” it argues. “This includes launching public education campaigns in collaboration with civil society organizations to highlight the wide-ranging dangers posed by synthetic media.”

“Only with an integrated strategy can society confront the risks posed by these technologies in a way that is ethical, effective and democratic.”

Article Topics

biometrics  |  deepfake detection  |  deepfakes  |  digital trust  |  generative AI  |  Reality Defender