Using OSINT Techniques to Detect Deepfakes and AI Fakery

We’re entering a time when seeing is no longer believing. A politician can appear to say something they never said. A journalist’s face can be copied into a fake confession. And AI-generated news anchors can read stories with eerie realism. This isn’t the future it’s the now. For OSINT practitioners, that means fakery isn’t just expected, it’s assumed.

But there’s good news. With the right techniques, even the most polished deepfake leaves fingerprints. You don’t need advanced forensics labs - just curiosity, patience, and a few well-tested methods.

Understanding the Landscape of Fakery

Most fake media today falls into two buckets. The first is video and audio deepfakes, where someone’s face, voice, or gestures are artificially synthesized. The second is synthetic content completely AI-generated images, fake screenshots, or even entire documents.

Both categories rely on public trust. If a video looks clean, if a quote is framed correctly, we tend to believe it. That’s why deepfakes spread fast. But also why they break trust when exposed.

This is where OSINT methods step in. The goal isn’t to disprove everything. It’s to verify what’s real, and raise flags where something doesn’t feel right.

Telltale Signs in Media Files

A good first step is always visual inspection. Many deepfakes still struggle with eye blinking, lighting inconsistencies, or mouth movements that don’t match the voice. Look closely. Do the shadows match across frames? Are there motion glitches in fast transitions? Does the voice sound hollow or too perfect?

Metadata can also help. Some manipulated media strips out creation data. A lack of EXIF information, or oddly generic timestamps it allcan be a sign that something was generated, not captured.

Reverse image and video search tools are another essential. Run the frames through TinEye or InVID. See if they’ve appeared before, especially under different captions. Context drift is one of the most common red flags.

Context Is the Hardest Thing to Fake

One reason fakes get spotted is because they try to exist outside context. A politician appears in a room they were never in. A crowd cheers in a city that didn’t host the event. The clothing, lighting, or architecture is wrong for the alleged location.

That’s why location-based and timeline-based OSINT still matter. If the person shown couldn’t possibly be where the video claims - or if they made a contradictory public statement - the fake begins to unravel.

This is where digging into older content helps. The past, when properly preserved, acts as a comparison tool. We've seen this used brilliantly in archives like the VHS-era internet footage collection, where original media gives us a baseline for authenticity. Not everything in the past was high-resolution, but it was real.

Language Patterns in AI-Generated Content

Deepfakes aren’t just visual. A growing number of text-based fabrications - from fake quotes to generated blog posts - now populate disinfo campaigns. You can often tell by tone: AI tends to be smooth but generic. Repetitive phrasing, overly formal statements, or strangely “neutral” opinions are common.

Compare this to real communication. Humans contradict themselves, make typos, change tone. AI rarely does, unless it’s been trained to mimic that style.

Again, historical baselining helps. What did this person or outlet sound like in the past? Did their style change dramatically overnight? Tools like archive.org or public tweet history can help you contrast voice and timing.

Ethics, Exposure, and Fragile Proof

The trickiest part of OSINT fakery detection is deciding when to go public. It’s one thing to suspect a video is fake. It’s another to explain why and have your audience believe you.

This is why documentation matters. If you're calling out disinfo, show your work. List timestamps. Link original sources. Keep a visual log of your checks. And be transparent about uncertainty.

When archives disappear, proof does too. That’s what makes preservation so crucial and what makes archive failure dangerous. In our post on what happens when digital archives collapse, we explored the risks of losing evidence just when it’s needed most.

Legal and Privacy Boundaries Still Apply

Not every fake is fair game. If you're analyzing material tied to individuals, especially private citizens, tread carefully. Some countries restrict the processing or storage of synthetic media. Others prohibit certain types of OSINT work altogether.

The boundaries are especially blurry with platforms like Instagram, where scraped content may violate terms of service. We’ve covered legal limits around archiving personal content, which are worth understanding before pulling media into your investigation.

If you're not careful, a fake might not just hurt its target, it might land you in trouble, too.

Seeing Through It With the Right Lens

In a world where image, sound, and even text can be synthesized, the role of the OSINT analyst has never been more important. You're not just looking for lies, you're searching for seams. Inconsistencies. Details that don't want to be seen.

Fakes will keep getting better. But so will our ability to detect them. The key is to stay skeptical, stay methodical, and trust the small clues because that’s where the truth tends to hide.