Real or Fake? A Practical Guide to Spotting Deepfakes
2026-03-22
Introduction
You have probably seen media content online and wondered whether what you were looking at was real. You are not alone, as millions of users worldwide face this uncertainty every day.
AI-generated content has effectively conquered the internet in recent years. Today's AI tools can create or modify audio, images, and videos in ways that look and sound highly realistic. What once required specialist skills and expensive hardware is now available to anyone through cheap or free apps and online services.
This explosion of generative AI brings serious risks and ethical concerns. Because highly realistic media can now be created by almost anyone, misleading content can reach large audiences quickly, especially on platforms that amplify what spreads fastest rather than what is most accurate.
However, not all AI-generated or AI-modified media qualifies as a harmful deepfake. The term is better reserved for content that appears authentic but misrepresents reality, for example by depicting people saying or doing things that never actually occurred, with the intent to mislead. Such material is particularly dangerous because malicious actors can use it for fraud, harassment, political manipulation, and other forms of criminal or deceptive activity.
These risks are amplified by the fact that our ability to detect manipulated media is often limited, especially when content is viewed briefly or out of context. The good news is that the landscape is improving. Some platforms and regulations now require that AI-generated content be disclosed or labelled, and machine-based detectors for synthetic media are getting better over time. But these systems are not perfect and will not catch everything.
This is where you come in. Humans still play a critical role in spotting context problems, understanding intent, and recognizing things that simply don't make sense. That is why learning a few simple checks gives you a real advantage when you scroll, read, or get forwarded content.
In this article, we focus on what you can realistically do to judge whether a piece of media is likely to be fake. No single trick is perfect, but a short checklist of audio, visual, and contextual checks will help you make better calls and reduce the chance of being misled.
Quick Checklist
Not every reader wants the full deep dive. If you just want to know what to look out for, here are the most important checks you can do right now.
1. Audio — Listen closely. Does the voice sound flat, robotic, or too perfect? Pay attention to breathing that seems missing or oddly placed, unnatural emphasis on words, and pitch that feels wrong. Also ask yourself: have I heard this exact voice before in other content? Many AI tools reuse the same default voices, so it might sound familiar for a reason.
2. Image — Look at the details. Zoom in on eyes, teeth, fingers, and hair. These are the areas where AI makes mistakes most often. Then check the shadows: do they fit the lighting in the scene? Also look at reflections in glasses, windows, or water. Do they show what you would expect based on the surroundings and the objects nearby?
3. Video — Check if audio and visuals match. Do the lips actually match the words? Is there background noise or room sound, or does the audio feel like it was added separately? Look at the background throughout the clip. Does it move naturally, or do objects shift, change shape, or disappear between frames?
4. Context — Verify before you share. Search for the event, quote, or claim on your own. Can you find the original source? Does what the person supposedly said match what you know about them? If you are still unsure, ask someone you trust. But be clear that you are not sure if it is real, rather than just forwarding it without comment.
5. Or just skip all of the above. Run it through our deepfake detector and grab a coffee instead. (We really tried hard not to put this here.)
Of course, our detector won't catch everything. And in general, relying on a single tool is never a good idea. That is why the following sections break down each of the checks above in more detail, with real examples and some interesting details about where generative AI still struggles.