Malicious use of deepfakes can be found in scams, misinformation, and disinformation

Deepfakes use AI to produce manipulated media to mislead viewers

Unlike most content created from large language and diffusion models, deepfakes intentionally misrepresent real people or events with the intent to deceive. As the products of this technology become more realistic, they pose increasing risks to public trust, security, and privacy.

Surreal backgrounds, asymmetry, and indecipherable text can help identify deepfakes

Differences in the backgrounds of training data with the same subject introduce variability, resulting in background textures and text that lack realistic detail. Difficulties with managing long-distance dependencies, the complexity of hair, and other real-world phenomena introduce errors that can be used to flag AI-generated content.

The ease of deepfakes creation may soon overwhelm our sense of digital truth

Shortly after the public release of related technology in 2017, some experts saw the technical limitations of deepfakes as preventing them from becoming widespread tools of disinformation. Since then, the neural networks behind diffusion models and generative AI have eliminated the barriers to creating convincing synthetic media for propaganda.

Deepfake potential ranges from destabilizing democracies to improving accessibility

The technology can revolutionize education, medicine, and art through actions such as helping a disabled person express themselves. However, fictional audio and video can also jeopardize a person's reputation and life or quickly spread misinformation, even without content being flawless recreations.

AI-generated avatars allow users to create deepfakes in real time

Synthetic media tools can map voices and faces to make individuals appear as someone else. Although they open the door to new opportunities in immersive education and entertainment, they also raise concerns about the misuse of personal identity.

Deepfakes came to prominence in the form of nonconsensual pornography

The explosion of deepfakes depicting renowned people in both ridiculous and compromising videos has provoked widespread fears they will undermine public trust in authoritative knowledge. However, some scholars argue that real videos will always be distinguishable from deepfakes via the video-sharer's testimony and a broader social context.

Once limited to expert studios, automated tools can create convincing deepfakes

Although the best deepfakes are engineered using generative adversarial networks where systems compete to create and assess fake data until the two cannot be distinguished, simpler tools have allowed nonexperts to develop deepfakes for financial scams, political deception, and nonconsensual pornography.

Audio deepfakes have fooled voice biometrics at banks

Limitations in facial and body movements as well as mismatches between audio and lip movements can sometimes help flag a video as a deepfake, but removal of the video component significantly improves AI mimicry.

Take a quiz to identify whether you can spot deepfakes

Advances in artificial intelligence have made deepfakes increasingly difficult to identify, as they are intentionally designed to appear real. The technology has exacerbated some of the problems brought on by digital editing tools like Photoshop.

Deepfake pornography poses significant challenges in combating sexual abuse

In 2017, 96% of deepfake videos involved image-based sexual abuse, which has disproportionately targeted minority women, LGBTQ individuals, adolescents and celebrities. Cultural norms, misogyny and AI advancements have contributed to the proliferation of explicit deepfake content.

Digital re-aging and de-aging of faces in film and TV has become increasingly common

Traditional techniques for accomplishing this process were slow and labor-intensive, and often required manual frame-by-frame edits, forcing them to be reserved for "hero characters" and close-up shots. Deep learning methods used by Disney and other production companies now provide fully-automatic and production-ready methods to produce such deepfakes for entertainment purposes.

Art and video produced through generative AI involve competing neural networks

Generative adversarial networks are a type of deep learning architecture that consists of a generator, which creates data that appear real, and a discriminator, which attempts to distinguish authentic from fake data. The two train in a competitive loop until the real and fictional data are indistinguishable.

Listen to a deepfake robocall of President Joe Biden

Bad actors are not solely using deepfake technology to manipulate images. They can also manipulate audio for political purposes. In this example, eligible voters are asked to skip the 2024 New Hampshire Democratic primary.

In China, deepfaking a dead loved one has become a growing industry

Digital replicas of the deceased provide individuals with another way to process grief in a culture with long-standing traditions of communicating with ancestors. The emotional and ethical implications of "digital immortality" are complicated further by cases of actors impersonating deceased relatives during deepfake video calls to hide their deaths.

Watch LinkedIn cofounder Reid Hoffman interview his deepfake doppelgänger

LinkedIn cofounder Reid Hoffman created an AI doppelgänger of himself in 2024 that was fed a sample of his voice and trained on his books and other writings and speeches. In this conversation, his deepfake version attempts to replicate human mannerisms, including scratching their nose and getting distracted.

Machine learning and deep learning are successive subsets of artificial intelligence

ML allows systems to learn from data, while DL uses complex neural networks to analyze large amounts of unstructured data. Neural networks enable capabilities like image and speech recognition through their use of algorithms to identify patterns in data.

Fair use may shield AI companies from potential copyright violations

AI models are trained on enormous datasets built from content scraped from websites across the internet. Copyright law includes a four-factor test to determine whether copies of content fall under fair use, but the law's enforcement has been inconsistent.

See the news with AI-generated data visualizations

Realtime uses generative AI to create immediate data visualizations of news items on its regularly-updated feed, pulling from public data feeds. Peruse stories around sports, economics, politics, and more with the helpful aid of charts and graphs. See the shape of the news with this clever AI tool here.

Neural implants blur the line between personal intention and machine execution

A key ethical dilemma—the contemplation conundrum—explores whether implants might misinterpret mere imagination as intent to act. Neuroscientists have found no brain signal that clearly marks when someone decides to act, making intent hard to detect in neurotechnological systems.

Famous Bigfoot footage stabilized using AI

Famous 1967 footage of a Sasquatch-like creature in a California forest has been stabilized using AI, clearly revealing the Bigfoot-esque creature to be a man in a suit.

Super recognizers may use specific facial features to commit faces to memory

Researchers believe that recognizing individuals seen only in passing, even decades later, is an ability of genetic origin and specific to facial recognition, rather than a result of better overall memory or perception. Applying eye tracking data from super recognizers to AI has led to improvements in facial recognition software.

Fraud and identity theft are among the most common dark web-related crimes

Credit card information, driver's license or passport scans, and login credentials are among the data bought and sold on dark web marketplaces, which can appear there without users' knowledge through data breaches. Medical records can fetch higher sums of money for their potential use in blackmail and insurance fraud.

The science of optical illusions

The human brain has developed cognitive shortcuts to help us navigate in a world where our visual senses are constantly bombarded of information. But these shortcuts can also be exploited, tricking the brain into telling us something is happening when it really isn't. This short video unpacks the neuroscience behind some of the most common (but perplexing) optical illusions.

Modern antivirus software searches for malicious behavior alongside malicious code

The first viral infections were identified by comparing the computer code of programs and files with a database of known virus codes, a method known as signature detection. The ongoing creation of new malware made databases continuously outdated, leading to the development of systems that detect patterns consistent with known viral activity.

The rise of the malicious use of drones

Drones are legally categorized as aircraft, so when homeowners see a drone hovering over their private property, it is illegal to shoot them down. As drones become more commercially available, their illicit use is also increasing. Celebrities have reported incidents of drone privacy invasions and events planners often deploy anti-drone systems to prevent incursions.

This site reaches millions of visitors who are tired of AI chatbots and seeking human connection. Pretend to be an AI chatbot and ask questions, provide answers, and draw for people in real time.

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