What Are Deepfakes and How to Protect Against Them
It seems like our world is drowning in deception. The number of deepfake files has skyrocketed from 500,000 in 2023 to a mind-boggling 8 million in 2025 – that’s a sixteen-fold increase in just two years!
Generated by Artificial Intelligence (AI), deepfakes distort reality in a video, audio, or image format, creating false scenarios that make a person say or do something they never did. From renowned politicians and celebrities in the entertainment world to ordinary people, almost anyone can fall prey to this malicious practice.
In an age where you can no longer trust your eyes or your ears, understanding this powerful technology is your first line of defense. Read on to learn exactly what deepfakes are and how you can protect yourself and your business from this rising tide of misinformation and fraud.
What Are Deepfakes?
Deepfakes are pieces of synthetic media (a video, an audio clip, or an image) created or altered by using AI technology, to replace someone’s face, voice, or actions with those of another person, making the result appear authentic even though it never actually happened.
A good example is this video that analyzes a deepfake of the former Canadian Prime Minister Justin Trudeau, in which he appears to recommend a book with a highly critical, politically charged title, “How the Prime Minister Stole Freedom”. Naturally, Mr. Trudeau never said any of this or recommended this book. This is just one of many examples of how deepfake videos can spread disinformation and seriously damage politicians’ reputations ahead of elections.
How Do Deepfakes Work?
Although the process of creating deepfakes may sound high-tech, the basic idea is pretty simple. The deepfake creators use AI to study how someone looks and moves, and then recreate that person’s face or voice in a way that feels real.
First, AI learns facial movements from real video clips. Then, it generates synthetic frames one by one to build the fake content. Finally, synthetic voice is layered on matching speech for extra realism.
But, for the sake of clarity, let’s break down the process of creating deepfakes even further.
STEP 1: Training the Neural Network
To get started, you need to collect data, as the AI needs plenty of images and videos of the person it’s meant to imitate. The more high-quality footage it has, the more convincing the final result can be.
Next, these visuals are used to train a deep learning model, often something called a Generative Adversarial Network (GAN). A GAN works like a creative rivalry:
- The generator tries to create fake images.
- The discriminator tries to tell the difference between the fake images and the real ones.
As they compete, both get better, and the generated images become more and more lifelike.
STEP 2: Generating the Deepfake
Once the model understands how the target person looks and moves, it can map those features onto someone else in a source video, i.e. swap faces. At this stage it’s important to make the new face mirror every expression, blink, and micro-movement in a natural way.
Finally, to make everything feel seamless, the system fine-tunes details like lighting, color, and resolution, so that the final video or audio clip blends smoothly with the original scene.
The Real-World Impact of Deepfakes
Deepfake technology isn’t just a fascinating trend. Unfortunately, it seriously affects and even is able to reshape a lot of things in our lives. Even though some would say – deepfakes can be a fun expression of people’s creativity, yet the damage risks outweigh the fun element.
Here’s how the deepfake technology shows up in different parts of our lives:
Entertainment: Film, Parody, and Creative Play
First, deepfakes have quickly made their way into the entertainment world.
In movies and on TV, the age of actors can be significantly altered, historical figures can be recreated, and performers can even “appear” in scenes they never shot. Also, creators use deepfake tools to produce comedic sketches or parody videos by blending famous faces into imagined scenarios. And digital artists are experimenting with identity, motion, and realism, using the tech as a new canvas for expressive or thought-provoking projects.
For example, one of the practical (i.e. more innocent) applications of deepfake technology is its use in digital marketing campaigns to create personalized video advertisements that address viewers by their name or tailor the message to their region.
Education
Of course, deepfakes don’t always appear with malicious intent; they’ve also opened the doors to new forms of learning and creativity. They can be used in educational reenactments. Teachers and museums have begun using controlled deepfake-style tools to bring historical figures to life in videos that feel more engaging than traditional lectures.
Privacy Risks
Sadly, the same technology that fuels creative innovation can also be (and often is) misused.
The most common case is the creation of non-consensual content that may be used for harassment and blackmail. Deepfakes can be weaponized to create explicit material involving people who never took part, thus violating people’s privacy. By far the most harmful use of the deepfake technology, such content, especially fake videos, pose serious risks to people’s mental health and reputation. More so, realistic copies of someone’s face or voice can make it easier for bad actors to attempt identity theft or social engineering scams.
EXAMPLE: Security systems are also not immune to deepfakes, as a BBC reporter demonstrated she could bypass her bank’s voice-authentication system using an AI clone of her own voice.
Fraud, Misinformation, and Disinformation
Deepfakes also play a growing role in the spread of false information and fake news.
- Political manipulation. Synthetic videos or audio clips of public figures can be used to stir controversy or shift public opinion.
- Fake endorsements and scams. Fraudsters may use deepfake voices or deepfake videos to impersonate leaders, celebrities, or executives to deceive the public or employees.
- Erosion of trust. As deepfakes get more realistic, it becomes harder to rely solely on visual or audio evidence, putting pressure on journalists, platforms, and the public to verify content.
EXAMPLE: Back in 2024, the Hollywood actor Tom Hanks publicly warned fans about a deepfake version of him being used without permission in a dental ad. He hadn’t filmed it at all, but the AI-generated video looked convincing enough that he felt the need to speak out.
Deepfake Risks
Deepfakes come with real-world consequences. With convincing synthetic videos and voice clips on the rise, the risks grow across industries like banking, government, and identity verification. Let’s review the biggest threats:
Identity Theft and Impersonation
- Financial fraud in banking
Fraudsters already use realistic deepfakes to impersonate someone else, such as an executive or an account holder, to gain unauthorized access to bank accounts, perform fraudulent transactions, or apply for loans in someone else’s name. A study shows that the average loss per company from deepfake-enabled fraud amounts to $603,000.
Deloitte estimates that generative AI-enabled fraud losses in the US alone could reach $40 billion by 2027. Source
- Identity verification (IDV) threats
Financial institutions and online services that use face-matching, voice biometrics, or live video checks are being targeted. Deepfakes help attackers bypass these systems by swapping faces or synthesizing voices. - Governmental and corporate risks
Deepfakes are also used in corporate espionage or manipulation. Thus, a convincing video of a CEO giving an order that turns out to be entirely fake can affect stock prices, insider trades, or sensitive government decisions.
In January 2024, an employee in Hong Kong transferred $25 millionafter participating in a video call where his “CFO and other colleagues” were all synthetically generated. Source
Social Engineering Attacks
- Phishing with deepfake audio/video
Phishing is more than just a dodgy email anymore. Attackers have gotten more sophisticated and use deepfakes to clone the voice of your boss on a “live” call, or to fake a video with a co-worker to convince you to click a link or share your credentials.
In 2025, it was reported that human detection rates for high-quality video deepfakes are as low as 24.5%. Source
- Business email compromise (BEC)
The technology behind deepfakes makes BEC attacks even more potent. If an attacker can simulate the voice of a CFO or create a fake video directive, employees may initiate fund transfers or release confidential data without the usual checks. For example, voice-clone scams, where victims confirmed a loss, had a 77% rate of financial loss.
Reputation Damage and Misinformation
- Fake news and public figures
Malicious deepfakes can show a politician or a public figure saying or doing things they never did, destroying trust, especially during elections. For example, in 2024, Donald Trump shared several AI-generated deepfake images on social media, including ones of Taylor Swift endorsing his presidential campaign and Kamala Harris at a communist rally. Such actions contribute to the spread of online election disinformation and increase overall public skepticism about authentic media. - Character assassination
However, those are not just public figures who can suffer from deepfakes, but ordinary people as well. A fake video or an audio clip showing a compromised situation can tarnish reputations, jeopardize careers, or cause emotional damage. That’s why governments and social media platforms are grappling with how to regulate non-consensual deepfake content.
Extortion and Blackmail
One of the most malicious uses is creating a synthetic video or audio clip that shows someone in a compromising scenario (deepfake pornographic videos) and then threatening to release it unless money or favors are provided. Such false evidence and coercion attacks exploit both the victim’s fear of exposure and the realism of the fake content.
In sectors like identity verification, even the suggestion that someone might be targeted by such a fake clip can weaken trust and force increased reliance on costly verification measures.
49% of businesses reported experiencing video (or audio) deepfake fraud, up from 29% in 2022. Source
Combating Deepfakes
But not only do deepfakes become more sophisticated in their complexity and realism, so do the efforts to detect them. Ways we combat deepfakes have also evolved.
Researchers and tech companies are actively developing algorithms and tools to identify deepfake media, often by analyzing inconsistencies in the video or by detecting digital fingerprints left by the creation process. There is a growing call for legal and regulatory frameworks to address the ethical and legal challenges posed by deepfakes.
For example, Know Your Customer (KYC) processes are critical for verifying the identities of clients to prevent fraud, money laundering, and other illicit activities. Let’s take a closer look at several ways KYC processes can protect your business against deepfakes:
Advanced Biometric Verification to Prevent AI Impersonation
Liveness Detection. Modern identity verification solutions include liveness detection techniques to ensure that the biometric sample, like a face or voice, is from a live person present at the time of the capture. This can involve asking the user to perform certain actions (blink, smile, turn their head) or using hardware sensors to detect subtle movements that are difficult for deepfakes to replicate convincingly.
Multi-Factor Biometric Verification. Combining different types of biometric data, such as facial recognition, voice recognition, and fingerprint scanning, can make it more challenging for deepfake technologies to spoof all these biometrics simultaneously.
Cross-Referencing Data Across Identity Sources
Document Verification. Matching biometric data with official documents (passport, driver’s license) that include embedded security features (holograms, watermarks) can help verify the authenticity of the user.
Database Checks. Verifying the provided information against trusted databases (government records, credit bureaus) can help identify inconsistencies that might indicate fraudulent activity.
AI and Machine Learning for Deepfake Detection
Deepfake Detection Algorithms. Implementing machine learning models specifically trained to detect deepfakes can help identify manipulated media. These models analyze subtle artifacts and inconsistencies in the data that are often indicative of deepfake technology.
Continuous Learning. The system can continuously learn and adapt by incorporating new data and techniques for detecting the latest deepfake technologies.
Behavioral Biometrics: Detecting Subtle Inconsistencies
User Interaction Patterns. Analyzing how users interact with the system (typing speed, mouse movements, navigation patterns) can help in building a unique user profile. Deviations from this profile can trigger further verification steps.
Keystroke Dynamics. Monitoring typing patterns, which are hard to replicate by deepfake technology, can provide an additional layer of security.
Regular Security Audits and Model Retraining
Security Audits. Regularly auditing the KYC process and updating the security protocols can help in identifying potential weaknesses and ensuring that the system is resilient against emerging threats.
Software Updates. Keeping the software and systems up-to-date with the latest security patches and improvements can mitigate vulnerabilities that could be exploited by deepfake technology.
Educating Users About Deepfake Threats
Informing Users. Educating users about the potential risks of deepfakes and how to recognize suspicious activities can empower them to participate actively in safeguarding their identities.
Feedback Mechanisms. Providing channels for users to report suspicious activities or concerns can help in early detection and response to potential threats.
By combining these strategies, KYC processes can be more resilient against the sophisticated threats posed by deepfake technologies, ensuring a higher level of security and trust in the identity verification process.
Looking Ahead: The Future of Deepfakes
Deepfakes are evolving at a speed that makes today’s challenges feel like only the beginning. Faster, cheaper, and more accessible AI models will make deepfakes more real and convincing. As a result, deepfakes will increasingly influence politics, financial security, personal privacy, and public trust, especially as fraudsters continue to weaponize them in social engineering and identity-related attacks.
But the future isn’t all bleak. Detection tools powered by AI, stronger KYC and biometric safeguards, and clearer global regulations are advancing just as rapidly. Moreover, organizations are beginning to treat synthetic-media threats as a core cybersecurity issue rather than a fringe concern. At the same time, public awareness is rising, and users are becoming more skeptical, informed, and proactive.
In the years ahead, the real goal won’t be eliminating deepfakes but building systems, policies, and habits that make them far less effective.
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