Deepfakes are among the most disruptive phenomena artificial intelligence has generated in recent years. Understanding what a deepfake is, how it works, and how to detect it is no longer a technical matter reserved for experts; it’s a digital survival skill for anyone or any company operating in connected environments.
In this guide, updated to 2026, you will find the complete definition of deepfake meaning, the types that exist, real deepfake examples, and the most effective tools to detect them before they cause harm.
What are deepfakes?
The term “deepfake” comes from combining two words: “deep learning” and “fake.” It refers to media content manipulated using artificial intelligence algorithms, specifically deep neural networks, to create a highly realistic version of something that didn’t happen.
In other words, deepfakes let you change faces, voices, or even movements in videos and audio, making it seem like someone said or did something that never happened. For example, you might see a celebrity singing a song they never performed or a politician giving a fake speech.
How do deepfakes work?
Deepfake AI is generated using advanced deep learning technologies. This involves training the AI with large amounts of data, such as images, videos, and audio of a person so that the machine learns to replicate their gestures, tone of voice, and facial expressions.
This process uses techniques such as GANs (Generative Adversarial Networks), which pit two neural networks against each other: one generates fake content, and the other evaluates its authenticity, improving the result with each iteration.
Thanks to tools available online, creating fake images has become easier. In the past, creating a deepfake required advanced programming skills and expensive equipment, but today, anyone with access to certain applications can generate a manipulated video.
How does deepfake AI work?
Deepfake AI is based on two main technologies that work in parallel:
GANs: Generative Adversarial Networks
The most widespread mechanism for creating deepfakes is the generative adversarial network (GAN). These work by pitting two neural networks against each other; one generates the fake content, and the other evaluates it until the result passes detection. Each iteration improves the quality of the deception.
Diffusion Models: the new generation
Since 2023, diffusion models (such as those powering Stable Diffusion or Midjourney) have partially replaced GANs in generating still images. They are more stable during training and produce higher-resolution results. In 2025-2026, multimodal models like Sora (OpenAI) or VideoPoet (Google) brought this technology to full-video synthesis.
How much data does a deepfake need to function?
In 2020, creating a convincing deepfake required between 50,000 and 100,000 images of the target person. By 2026, the latest models can generate a believable animated video from a single high-resolution photograph. This has democratized the process and exponentially increased the risk.
Why are deepfakes dangerous?
Now that you know what deepfakes are and how they work, we must emphasize that although this technology has positive applications, such as in entertainment, education, or marketing, its dark side is undeniable. Deepfakes have been used to spread fake news, impersonate identities, and even extort people.
Impact on society
- Spreading disinformation. Deepfakes have become perfect tools for disinformation campaigns. Manipulating a video to make someone believe something false can have serious consequences, especially during elections or social crises.
- Reputational damage. A fake image or video can ruin the reputation of a public figure or anyone, affecting their personal and professional life.
- Cyberbullying and fraud. Deepfakes have been used to create non-consensual content or to trick people into fraudulent activities.
Critical threats from deepfakes
The risk of deepfakes is not only technological; it is also economic, reputational, and social. These are the most critical threats identified in 2026:
Financial fraud and Business Email Compromise (BEC)
Audio and video deepfakes have become the most sophisticated attack vector in next-generation BEC fraud. An attacker can clone the CEO’s voice or simulate a corporate video call to authorize fraudulent transfers. The FBI and Europol report that this type of fraud grew by 200% between 2023 and 2025, though exact figures vary by source.
Disinformation and reputational damage
A deepfake can destroy a person’s or company’s reputation in minutes. Content can go viral before it can be debunked. For companies, deepfake-driven reputational damage can be equivalent to months of public relations work.
Identity theft in authentication
Facial verification systems used in banking, insurance, or access to corporate platforms are vulnerable to high-quality deepfakes.
Impact on people’s safety
On an individual level, deepfakes are used for cyberbullying, sex extortion, and identity theft. Platforms such as Qondar allow citizens to monitor whether their image or digital identity is being used without consent.
The 6 steps to detect a deepfake
Although they are becoming more and more realistic, deepfakes are not infallible. A few tricks can help you spot these fake images and protect yourself from deception.
- Notice the details of the face: Deepfakes often fail in subtle aspects, such as eye blinking, lip-syncing, or natural eyebrow movement. If a video seems strange, pay attention to these details.
- Analyze the audio: In manipulated audio, the intonation and rhythm of speech can sound mechanical or unnatural. Listen carefully if something doesn’t fit.
- Look for lighting inconsistencies: Errors in shadows or reflections are common in deepfakes. It is likely false if the face’s lighting does not match the environment.
- Use specialized tools: Currently, platforms are designed to analyze whether a video or image has been manipulated. Cybersecurity tools such as Deepware Scanner or InVID can help verify content authenticity.
- Trust official sources: Verify the information with reliable sources before believing dubious content. In many cases, deepfakes are designed to manipulate you emotionally and provoke immediate reactions.
- Use specialized detection tools. AI-based deepfake detectors analyze the digital fingerprints left by generative models. These tools fall under what cybersecurity companies call next-generation cybersecurity tools designed to detect synthetic content.
What to do if you find a deepfake?
If you suspect you have encountered a deepfake or that you are a victim of one, take the following steps:
- Don’t share it: Spreading false content, even with the intention of denouncing it, amplifies its reach.
- Document the evidence: Download the content, save the URL, and note the date and context before it is deleted.
- Report on the platform: All major social media platforms have reporting mechanisms for manipulated, synthetic content. Use them.
- Notify the affected person: If the deepfake impersonates a known person, inform that person directly so they can take legal action.
- Contact INCIBE or your national Cybersecurity Agency: Spain’s National Cybersecurity Institute and all the National Cybersecurity Agencies offer assistance to citizens and companies affected by digital security incidents, including deepfakes.
In the business context, deepfakes are an attack vector that must be considered in email security and identity-verification strategies.
Deepfake AI and regulation in 2026: what changes
The European Union’s AI Act, in force since August 2026, establishes specific obligations for providers of generative AI systems. Among them is the obligation to label synthetic content with detectable digital watermarks. This measure, although relevant, does not solve the problem of deepfakes created with tools outside of European regulatory control.
In Spain, the GDPR and the Organic Law on Data Protection already allow individuals to file complaints with the Spanish Data Protection Agency (AEPD) regarding the non-consensual use of their image through deepfakes. However, attributing deepfakes to a specific author remains the main legal challenge in 2026.
The convergence of deepfakes with other threats such as SIM swapping, ransomware, and AI in cybersecurity is creating increasingly complex attack scenarios that require coordinated responses.
How can we help you at Enthec?
At Enthec, we know the challenges deepfakes pose to individuals. We provide cyber surveillance solutions that help people detect and prevent digital manipulation.
The impact of deepfakes is real, but with the right solutions, you can stay one step ahead. Contact us and protect what matters most: your credibility and security.
In a world where making someone believe something false is true has become so simple, prevention and knowledge are your best allies. Don’t let deepfakes fool you: identify, protect, and take action
Trust Enthec to keep you safe in the digital environment.
Frequently asked questions about deepfakes
What exactly do deepfakes mean?
The term “deepfake” combines “deep learning” and “fake.” It refers to any multimedia content generated or manipulated using artificial intelligence to impersonate a real person’s appearance or voice without their consent.
Can a deepfake be made of anyone?
Technically, yes. By 2026, a single high-quality photograph could create a compelling animated video. That’s why it’s important to limit the public exposure of high-resolution images and implement digital identity monitoring protocols.
Are deepfakes illegal in Spain?
Using deepfakes without consent can be punishable under the GDPR, the Spanish Organic Law on Data Protection, and, in cases of harassment or extortion, the Spanish Penal Code. The European AI Act, in force since 2026, adds further obligations for creators of synthetic content in generative AI.
What is the difference between a deepfake and a shallowfake?
A deepfake uses artificial intelligence and neural networks to generate highly complex synthetic content. A shallowfake, on the other hand, is a simpler manipulation, such as slowing down a video, cutting out context, or editing subtitles, which doesn’t require AI but can be just as deceptive.
How can I tell if a video of a video call is a deepfake?
In a real-time video call, watch for video quality issues (live deepfakes often have higher latency or inconsistent resolution), head movements at extreme angles (which current models distort), and unnatural facial expressions. In critical corporate environments, always establish a pre-agreed verification word with your contact.
Can deepfakes affect companies as well as individuals?
Yes, and with serious economic consequences. Deepfakes of executives, employees, or brand representatives are a growing vector of corporate fraud. Corporate cyber surveillance is the first line of defense for detecting these types of threats before they materialize.
