Authenticity in the Age of Deepfakes
Deepfakes, which are machine-generated images, audio, and video, have rapidly evolved from obscure novelties into significant threats due to developments in AI technology, posing risks such as disrupting elections, compromising financial security, and infringing on personal dignity and privacy.
When it is possible to fake almost anything, even genuine evidence has the potential to draw suspicion due to an atmosphere of distrust and skepticism. People begin to pause and second-guess. As this hesitation spreads, our shared sense of reality slowly starts to unravel.
As people grow increasingly uneasy about whether online content can be trusted, some have begun looking to blockchain as a way to keep tabs on where images and videos come from and how they’ve been altered over time. The idea is that by leaving a visible trail of changes, it becomes harder for manipulated or fake media to pass as the real thing. Rather than hunting for forgeries after the fact, authenticity would be verifiable at the moment content is produced.
Technically, this works by assigning each file a distinct digital fingerprint. That fingerprint is cryptographically signed and connected to a blockchain entry. The record may include details such as time, device, or location. Any alteration changes the fingerprint. Verification is binary: the content either aligns with the record, or it does not. Authenticity, in this sense, is mathematically proven.
Building on these foundations, this approach underpins a growing ecosystem of provenance initiatives.
Blockchain’s main power is in transparency. It reveals when a file was created, whether it was modified, and who signed it, assuming, of course, that the signers are trusted. By exposing tampering, it raises the cost of manipulation and limits plausible deniability. Blockchain does not generate truth on its own, but it can constrain concealed dishonesty.
For all the promise that blockchain brings, it still has distinct parameters. Where it truly excels is in tracking where a piece of media originates and whether it has been altered along the way. What it cannot do, however, is determine the truth of what is being presented.
A video might have a clean, well-documented history and still depict something that never actually occurred. Even an AI-generated clip can be fully traceable while telling a completely false story. In that sense, blockchain answers a practical question of “has this file stayed the same?” which does not undermine its value, but it does set clear limits on what the technology is able to handle and where it’s best suited.
Practical challenges are hard to ignore. Media is created at a global scale, and logging every asset on-chain would be costly, slow, and difficult to justify environmentally. In response, researchers are experimenting with hybrid approaches such as off-chain storage, selective anchoring, and permissioned ledgers, which improve scalability but also introduce added technical and governance complexity. Adoption remains the most serious obstacle: provenance systems only create trust when they are widely used, yet large volumes of legacy media, anonymous uploads, and content from unsupported devices will inevitably lack credentials. In such fragmented environments, missing proof can easily be mistaken for proof of manipulation, opening new avenues for abuse rather than reducing it.
Privacy concerns further complicate the picture. Permanent records of authorship or location could expose creators to surveillance or retaliation. Decisions about who qualifies as a “trusted issuer” risk concentrating authority in large institutions while marginalizing independent voices. Systems intended to reinforce trust could, if poorly designed, deepen exclusion instead.
While blockchain offers key resources for verifying digital content, it is not a cure-all solution. Its effectiveness depends on integration within a wider framework that includes specific safeguards, such as digital literacy programs and regulatory measures like international standards for media verification. Joint initiatives among technologists, policymakers, and the public are essential to improve security and privacy.
Systems such as those that prove provenance have the ability to assist in confirming authenticity, but they do not automatically create trust. On its own, blockchain won’t make deepfakes disappear. Progress is more likely when technologists, policymakers, platforms, and the public work together to achieve increased vigilance, standards, and find new approaches for security and privacy.
