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Deepfakes are a rapidly growing concern in the digital world, as they have the potential to cause significant harm to individuals and society as a whole. As artificial intelligence and machine learning technologies become more and more complex, creating realistic and convincing deepfakes is becoming easier day by day. Deepfakes are manipulated videos or images that can be used for a variety of purposes, including spreading misinformation and identity theft. In fact, they have the ability to spread propaganda and misinformation that could undermine people’s trust in institutions, incite chaos and confusion, and upend the very fabric of our society. In this article, we’ll explore the dangers of deepfakes and provide practical tips on how to detect deepfake videos.
¿Qué es un Deepfake?
We can define deepfake as a type of synthetic media that has been manipulated or generated using artificial intelligence (AI) algorithms. In other words, deepfakes use machine learning to create or alter audio, video, or images to depict events that did not occur or to portray individuals saying or doing things that they did not actually do.
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If you have seen videos of Mark Zuckerberg boasting about his total control of billions of people’s stolen data, Barack Obama using offensive words to describe Donald Trump, or Kit Harrington delivering a heartfelt apology for the disappointing finale of Game of Thrones, then you already have an idea about deepfakes. Deepfakes are the modern-day equivalent of photoshopping. These altered and manipulated digital media like videos, audio, and images can cause harm to both individuals and organizations, just like computer viruses. Identifying good deepfake videos can be exceptionally challenging; however, in contrast to computer viruses, anyone can produce good deep fakes.
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Los expertos han identificado los tres tipos o métodos de deepfake más peligrosos. El primero de ellos son las voces deepfake o deepfakes de audio. Se trata de una voz generada por IA que imita de cerca la voz de una persona real. El audio deepfake es actualmente la técnica más lucrativa empleada por los estafadores para la pesca de voz deep fake. Por ejemplo, en 2019, los delincuentes utilizaron tecnología de voz clonada para extorsionar al director general de una empresa energética británica por 243.000 dólares.
El segundo método son los videos deepfake, que se pueden usar para manipular la opinión pública y lavar el cerebro de las personas al presentar información falsa de manera convincente. Los infames videos en los que aparecen Barack Obama y el presidente Trump son ejemplos de este tipo de videos. También se pueden usar para crear videos falsos de personas comunes, especialmente para crear pornografía de venganza.
This AI technology can also produce deep fake text that convincingly mimics the writings of real people as well as genuine social media posts. These fake accounts may look authentic and even attract followers over time. A group of these accounts can be easily used to slander a company or a product. The only way to differentiate such AI-generated text is to identify patterns in the language used, such as word choice and sentence structure, and detect inconsistencies.
Peligros de la tecnología deepfake
En el mundo actual de noticias falsas y desinformación, las personas utilizan la tecnología deep fake para difundir información errónea, crear narrativas falsas, crear una imagen pública específica, censurar o burlarse de las personas y crear contenido pornográfico.
Deep fakes often target celebrities. Their fame and availability in media make them perfect for memes, and AI technology can easily create convincing deep fakes of them. A few years ago, TikTok was flooded with videos featuring actor Tom Cruise engaging in activities that were unusual for him, such as fooling around in an expensive men’s fashion store, exhibiting a coin trick, and playfully growling while singing a brief version of “Crash Into Me” by the Dave Matthews Band. Another example is the video of Yong Mei, a famous Chinese celebrity that went viral a few years ago. In this video, she was inserted into a 1983 Hong Kong TV series. The video got 240 million views before it was eventually removed by Chinese authorities. However, the creator, who was a fan of Young Mei, apologized publicly on Weibo and said he made the video to raise awareness about deepfake technology.
In politics, deepfakes can be used to create fake videos or images of political figures saying or doing things that they did not actually say or do. This can be done to spread misinformation or damage someone’s reputation. In 2019, a video of Nancy Pelosi was edited to make it seem like she was speaking oddly and looking drunk. It was shared globally, but Facebook did not remove it after being proven fake. Videos on Donald Trump’s story about a reindeer and Barack Obama’s public service announcement are other examples of this type of fake video.
Did you know that 96% of deep fakes are pornographic? A significant majority (99%) of the faces used in these manipulated videos belong to real women, including non-celebrities. The use of deep fakes can allow a person to effortlessly superimpose the image of an ex-girlfriend onto that of a porn actor and circulate it within their social circle. As a result, the victim may face the negative effects of being falsely accused of a taboo act, even if it never happened.
Deepfakes are also increasingly being used in financial scams. These scams typically involve scammers creating deepfakes of important people like CEOs or politicians and then using them to trick people into transferring money or sensitive information. For example, a deepfake video of a CEO might be used to persuade an employee to transfer money to a fraudulent account. In 2019, a group of scammers used the voice of a CEO of a UK-based energy company to impersonate him and trick an employee into transferring $243,000 to a fraudulent account.
Otro aspecto peligroso de los deep fakes es que hay muchas herramientas sencillas disponibles en línea para crear deep fakes. Según Ben Coleman de Reality Defender, cualquier estudiante de secundaria puede crear un deep fake usando un iPhone de cinco años.
Algunas aplicaciones éticas de los deep fakes
It is essential to remember that deep fake technology can have ethical applications in today’s world. One such example is enhancing the image quality of old or low-resolution videos. Instead of using the traditional upscale method, this technology redraws the image, allowing for changes in image quality. Additionally, deep fake technology can synchronize speech with lip movements, known as lip syncing. Perfecting lip movements to match any linguistic articulation would be a significant advancement in the field of film and TV dubbing. A practical example of this is the PSA featuring David Beckham aimed at combating malaria. As Beckham speaks nine languages, deep fake technology was used to adjust his lip movements to each language.
El potencial del deep fake también se extiende a la revivencia de artistas fallecidos como Salvador Dalí en el Museo Salvador Dalí en Florida. Además, una aplicación única de esta tecnología es animar el arte. Por ejemplo, el laboratorio de investigación de IA de Samsung ha permitido que la Mona Lisa exhiba movimientos en su cabeza, ojos y boca. En el futuro, esta tecnología podría ahorrar tiempo y dinero a la industria cinematográfica.
Cómo identificar los deepfakes
Busca inconsistencias en el video
Los deepfakes suelen tener sutiles inconsistencias que los delatan. Por ejemplo, es posible que la iluminación, las sombras y los reflejos no coincidan con el entorno, o que los movimientos de la persona parezcan un poco antinaturales.
Una señal de advertencia común de un video o imagen deepfake son los ojos antinaturales, especialmente si los ojos de una persona se ven extraños o si no parpadean. El parpadeo y los movimientos oculares naturales son difíciles de falsificar, por lo que si alguien parece estar mirando sin parpadear o sus movimientos oculares se ven extraños, podría ser una señal de que el video ha sido manipulado. Además, cuando las personas hablan entre sí, sus ojos generalmente se mueven de forma natural y es difícil imitar eso con precisión en un video falso.
An unpropotional face and body is another sign of a deepfake. This is because the AI algorithms that create deepfakes can struggle to replicate the proportions of a person’s face and body accurately, which results in a distorted or exaggerated appearance. If a person looks strange or weird when they turn their head to the side, or if their movements seem disconnected or unnatural from one moment to the next, it might be a sign that a video has been changed. Mismatched skin tones and oddly placed shadows are two other important clues.
Comprobar la calidad del audio
Los deepfakes pueden tener un audio que no coincide con el movimiento de los labios de la persona o el tono de su voz. Escucha atentamente para ver si el audio suena natural y coincide con el contexto del video. Al hacer deepfakes, los creadores suelen prestar más atención a las imágenes que al audio. Los signos de audio falso pueden incluir movimientos de los labios que no coinciden con las palabras habladas, voces que suenan robóticas, pronunciación de palabras extrañas, ruido de fondo digital o incluso una ausencia total de audio.
Utiliza la tecnología para identificar los deep fakes
The advancements in deep fake technology have led to the development of protection tools. The University of Buffalo has created a tool that automatically identifies deep fake photos. This tool examines the light reflection in the eyes to distinguish between real and fake photos. Generally, when we look at something, the image we look at is reflected in our eyes, so in genuine photos, both eyes show the same shape and color of reflection. However, computers cannot generate identical reflections in the eyes since they combine multiple photos to generate one.
Moreover, Intel has introduced an AI tool named FakeCatcher (Cazador de falsificaciones) that can identify whether a video has been altered using deepfake technology in real-time. This technology claims to achieve a 96% accuracy rate and detect deepfakes within milliseconds. Unlike other deepfake detectors that rely on deep learning to analyze a video’s raw data for manipulation signs, FakeCatcher looks for human-like indicators, such as subtle changes in blood flow in the pixels of a video.
También hay otras herramientas para analizar el ruido digital y las fotos. La estructura del ruido natural y el generado por ordenador puede variar considerablemente, lo que nos ayuda a diferenciar entre fotos auténticas y falsas.
La Coalición para la Procedencia y la Autenticidad del Contenido, liderada por Adobe, Microsoft, Intel y la BBC, también ha desarrollado un estándar para diferenciar entre el contenido generado por computadora y el contenido real. Hasta que se adopte ampliamente, el potencial de los deepfakes para el uso malicioso es significativo.
Cómo mantenerse a salvo de los deepfakes
You should always exercise caution when consuming information online, especially when it comes to sensitive topics that can cause conflict or provoke intense emotions. Be alert to possible manipulations or distortions of facts that can be used to push a particular agenda or influence public opinion. Always seek out multiple independent sources of information to verify the accuracy of online content. Don’t rely only on video, photographs, or audio on someone’s profile, as these may be deep fakes or altered. It’s important to do your research and cross-check information from different sources to ensure its authenticity. Finally, stay away from synthetic social networks and fake social media accounts.
Para evitar ser víctima de deepfakes, asegúrese de no publicar información personal sobre usted en línea, ya que puede usarse para el robo de identidad u otros fines maliciosos. Tenga cuidado con el tipo de información que comparte y con quién la comparte, y siempre tome medidas para proteger su privacidad y seguridad en línea.
Es crucial educar a las personas sobre los peligros de los deep fakes y desarrollar nuevas tecnologías para detectar y combatir su uso. La creación y distribución de deep fakes debe ser regulada, y debe haber consecuencias legales para aquellos que usan deep fakes de manera maliciosa.
Conclusión
Deepfakes are a technology that can have severe impacts on people all over the globe, including damage to reputation, financial loss, and identity theft. The only way to stay safe from deepfakes is by understanding the technology behind deepfakes and using deepfake detection tools; we must always remain vigilant, verify information sources, and stay up-to-date about the latest developments in technology. Ultimately, the key to prevent the spread of deepfakes is through education and awareness, so let’s all stay informed and stay safe in this digital age.
If you enjoyed this article then you may also like to read nuestra guía completa de ChatGPT.
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