There once was a time when the digital world used to be predictable and most of the time what you saw on screen was real. But now that certainty is quickly disappearing. This is evident from the fact that Deloitte’s 2025 Cybercrime Outlook warns that AI-powered fraud, especially AI deepfakes, could drain more than US$40 billion a year by 2027.
Almost a few years back, deepfakes emerged as a fun activity on the internet but now it has evolved into one of the most dangerous threats to personal security, cybersecurity, and public trust.
Sam Altman, CEO of OpenAI, in his wide-ranging interview about the economic and societal impacts of AI at the Federal Reserve commented:
A thing that terrifies me is apparently there are still some financial institutions that will accept a voice print as authentication for you to move a lot of money or do something else. You say a challenge phrase, and they just do it. That is a crazy thing to still be doing… AI has fully defeated most of the ways that people authenticate currently, other than passwords.
This concern by Sams Altman and the current statistics about all the deepfake incidents which have risen by 1500% in the recent 2 years show that these scams aren’t just online pranks anymore. They are attacking people emotionally, financially, and psychologically.
When AI can pretend to be anyone you trust, it becomes hard to know what is real and what is fake. That’s why we must ask ourselves:
Can we still trust what we see online?
What Is a Deepfake?
Deepfakes are videos, pictures or audio clips made with artificial intelligence to look real. They can be used for fun, research, but sometimes they’re used to impersonate people in order to deliberately mislead people.
Using artificial intelligence, deepfakes can mimic a person’s voice and facial features. The technology uses an audio recording of someone’s voice to make it say things that the person might never have said.
Two generative AI tools; generator and discriminator; are required to create a deepfake image or recording. One tool creates the image or recording, and the other detects if the output is fake.
The generator (also known as the encoder) analyzes input data and extracts key features from the recording to produce outputs. These outputs are sent to the discriminator (also known as the decoder) to detect artificial outputs like a manipulated voice recording.
The generator and discriminator create a feedback loop that repeats until the desired quality of deepfake recording or image is created.
Most of deepfake videos or pictures look very strange, so they can be easy to spot as imitations. However, sometimes when they are produced by highly skilled professionals and more advanced technologies can look quite realistic. Such deepfake images can mislead people more easily
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Why Deepfakes Are Suddenly Everywhere
Deepfakes have spread across the internet because they’re now extremely easy to make. Not long ago, to create a fake video you needed expensive computers and advanced technical skills. But now, anyone with a phone and an internet connection can do it.
There are free apps and websites like Reface, FaceMagic, DeepFaceLab, Zao, FakeApp, DeepSwap, HeyGen that allow people to swap faces in seconds, without any experience and skill in generating fake images and videos. This is a very fair reason why we see deepfakes everywhere.
Another big reason why AI deepfakes are everywhere is because of our social media posting trend. Now, every person on social media posts lots of videos and images of their personal life on these platforms letting AI learn from this huge amount of data. This data trains AI models to mimic even minor details to make artificial images and videos look more real. And once a deepfake appears, platforms like TikTok, YouTube, and X spread it faster than anyone can verify it.
Another big reason deepfakes are spreading so fast is because the laws currently there are almost no laws and regulations against it. This means the technology keeps on growing freely without solid boundaries set by law.
Some laws are being discussed, but they aren’t fully active yet. The laws under discussion include, Outputs of GANs Act, the Deepfake Report Act, the DEEPFAKES Accountability Act, and the DEFIANCE Act. But the simple truth is none of these laws are fully in action yet. They are proposals, ideas, and discussions but not real protections.
So while lawmakers are still debating what to do, deepfake tools are improving every day. And because there are no strong rules in place, anyone can create a deepfake, share it online, and face little to no consequences.
This is why deepfakes feel like they’re “suddenly everywhere”… because they are.
Why Deepfakes Scare People
Deepfakes scare people because they make it difficult for us to believe what we see online. Before 2017, when deepfake was not there, we easily believed every video we watch and audio we hear as real. But deepfakes can copy a person’s face or voice so perfectly that we can’t tell the difference anymore.So we start to worry: “Is this true or is this fake?” And that feeling makes people scared and unsure.
Many people worry, “What if someone uses my face or voice without permission?” This thought alone creates fear and anxiety. Real videos and fake videos now look almost the same, so even smart people can be tricked.
Deepfakes also make people feel vulnerable. A fake video can ruin someone’s reputation, damage relationships, or even harm their career. It only takes one clip to destroy trust.
One of the scariest uses of deepfakes is in politics. AI can create fake speeches or videos of leaders saying things they never said.
For example, in January 2024, fake robocalls of President Biden encouraged voters in New Hampshire to stay home and not participate in the state’s primary.
So we can say, a single deep fake can cause confusion and can be used for bullying, revenge, or blackmail
This is why deepfakes scare so many people and open the door for scams and manipulation in ways we’ve never faced before.
The Harmless Side of Deepfakes
Deepfakes have been making headlines for all the wrong reasons. But they can be used for good purposes as well and have a constructive role to play in many fields like ecommerce personalization software, healthcare, art, history, etc.
Personalization
Deepfake technology is really useful for businesses. One of the positive uses is video personalization.
Businesses make separate videos for each customer to make them feel special and to earn their loyalty. Normally, it’s really difficult or almost impossible for a business owner to record hundreds of individual videos calling every customer by name.
But with AI, you only record one video, and the AI automatically creates many versions that sound like you’re speaking directly to each person. Tools like Maverick help ecommerce businesses communicate with customers making them feel special, valued, and connected.
Medicine and Healthcare
In medicine and healthcare, it is very difficult for doctors to train their AI system against rare health diseases because due to less patients of rare disease, less data is available about them. Moreover, if the data is available doctors cannot share real patient data because of privacy rules.
With AI, doctors can create fake medical images (like MRI scans). These deepfake realistic images help medical AI learn better and spot diseases more accurately.
For example, the Mayo Clinic used AI to create “fake” brain scans to help their system learn how to detect tumors.
Deepfakes could also be used in other practical medical settings to help patients who have lost motor, speech or visual abilities to communicate better. They can give such patients the power of better self-expression.
Art and History
Deepfake technology allows us to experience things that existed before our times and that we wouldn’t be able to comprehend otherwise.
For example, a Scottish company recreated a speech that John F. Kennedy was supposed to give on the day he was killed.
Similarly, Samsung Lab has developed AI that animates paintings like the Mona Lisa. This shows how AI video maker capabilities are changing our interaction with art. This fusion of technology and history not only improves educational experiences but also opens new avenues in video marketing and customer engagement for cultural institutions.
How Deepfakes Trick the Human Brain
Deepfakes fool us so easily because our brains are naturally built to trust what we see and hear. Since childhood, we learn that if a face looks real and a voice sounds familiar, then it must be real. We don’t question it and our brain instantly believes it.
AI takes advantage of this habit. Deepfakes copy a person’s face, expressions, and voice so well that our brain reacts automatically, without checking if it’s real.
Can You Actually Spot a Deepfake?
Detecting deepfakes is getting more difficult as the technology that creates deepfakes is getting more advanced day by day. In 2018, one year after the appearance of deepfake researchers demonstrated that deepfake faces didn’t blink like humans do, so it was the only way to detect if images and videos were fake or not.
As soon as the study was published, deepfake developers started fixing this drawback in deepfake, making it even more difficult to detect. So, the moment experts reveal how to spot deepfakes, creators use that information to improve their fakes even more. So instead of stopping the problem, the research accidentally teaches the technology how to get harder to detect.
But not all deepfakes are products of sophisticated technology. Poor-quality material is usually easier to detect because
- The lips may not sync with the words correctly
- Skin tone might look uneven
- Hair can look unnatural
- Teeth or jewelry may shine in odd ways
- Side-profile shots often look less realistic
As deepfake technology becomes more complex and harder to detect, more resources are becoming available specifically to help individuals detect deepfakes on their own while scrolling through social media.
This can be done by paying close attention to specific attributes like facial transformations, glares, blinking, lip movements, natural sounds like coughs or sneezes and other characteristics like beauty marks and facial hair.
Can We Still Trust What We See Online?
Today, trusting what we see online is getting harder but not impossible. Deepfakes and edited videos make it easy for anyone to create something that looks totally real, even when it’s fake. That’s why we can’t believe everything at a first look anymore.
Instead, we need to be a little more careful. Before trusting or sharing a video, photo, or voice note, take a pause and ask yourself: Does this seem real? Is the source trustworthy? Has this been reported anywhere else? A few seconds of checking can stop false information from spreading.
The most important thing to remember is this: just because something looks real doesn’t mean it is real. With deepfakes becoming more advanced, our best defense is awareness, skepticism, and smart verification.
FAQs about Deepfakes
What exactly is a deepfake?
Deepfakes are videos or images that often feature people who have been digitally altered, whether it be their voice, face or body, so that they appear to be “saying” something else or are someone else entirely.
Why are deepfakes suddenly everywhere?
Nowadays, deepfakes are everywhere because they are easy to make because of the availability of free apps and websites. Moreover, they can be spread instantly. Another big reason deepfakes are spreading so fast is because the laws currently there are almost no laws and regulations against it. This means the technology keeps on growing freely without solid boundaries set by law.
Why are deepfakes dangerous?
Deepfakes are dangerous because they can be used for scams, political lies, fake news, bullying, and reputation damage.
How can I tell if a video or photo is a deepfake?
There are many signs through which you can detect whether the content is deepfake or not.You can look for the following signs:
- The lips may not sync with the words correctly
- Skin tone might look uneven
- Hair can look unnatural
- Teeth or jewelry may shine in odd ways
- Side-profile shots often look less realistic