What Is Deepfakes

What are deepfakes?

Deepfakes refer to artificial media content, including images, videos, and audio recordings, generated through artificial intelligence (AI). This technology manipulates real data to produce content that closely resembles authentic media, making it challenging to differentiate between the two. A typical example is substituting a person’s face and voice in an existing image or video with another person’s features. This manipulation can result in highly realistic fake videos and audio clips that seemingly depict individuals performing actions or uttering words they never did in reality.

Deepfake creation often involves modifying a person’s facial features through reenactment, replacement, editing, or synthesis. Techniques such as face swapping, face transferring, facial attribute manipulations, or inpainting are commonly used. These methods lead to localized manipulations and are usually based on Generative Adversarial Networks (GANs). However, a new class of methods, known as denoising diffusion probabilistic models, has recently demonstrated remarkable generative capabilities, raising fresh concerns about the authenticity of daily internet images.

Can deepfakes be dangerous?

Deepfakes indeed pose a significant risk. The technology is widely accessible, and the potential for misuse in cybercrime, social media impersonation, political propaganda, and disinformation is alarmingly high. Malicious actors can exploit this fake content to fabricate false narratives, blackmail individuals, impersonate public figures for fraudulent activities, or tarnish individuals’ reputations.

Several methods are available to detect deepfake images or videos:

Visual inspection: Deepfake images and videos may exhibit specific artifacts or abnormalities absent in real videos, such as flickering, distorted images, or mismatched lip movements.
Metadata analysis: The metadata in a digital file can help trace its origin and verify its authenticity. Analyzing a video’s metadata can reveal whether the file has been manipulated or edited.
Forensics analysis: Forensic techniques, including video pattern analysis and audio and video comparison, can aid in detecting a deepfake.
Machine learning: Machine learning algorithms trained on extensive datasets of real and fake videos can help classify new videos as either fake or real.

As AI-generated and face-swapping videos become more sophisticated, assessing their legitimacy will become increasingly challenging. Therefore, a more accurate evaluation may require a combination of all the above methods.

Before discussing the specific visual clues of deepfakes, it’s essential to note that fake media files can be based on:

Digitally manipulated pictures and videos of real people to make it appear as if the individuals said or did things that never occurred
Completely new identities that do not exist in reality

Here are some things to look out for:

Visual and auditory artifacts
Unnatural synchronization of voice-mouth movement
Unnatural video appearance when slowed down
Image blurring, absence of shadows, artificial lighting
Inconsistencies in face symmetry, such as unnatural eyes, ears, teeth, hair, and skin that appears too smooth or too wrinkly
Unnatural blinking speed, eye movement, or lack of it
Unnatural reflection in eyeglasses that does not move synchronously with the individual’s face
Synthesized voice that pauses at inappropriate moments
Lower quality of generated voice (lower bitrate) or different pronunciation for certain words
Inconsistency between the message sent and the individual’s facial expression, or lack of emotion

Deepfakes have significant implications in the context of cybersecurity and online scams. The technology is globally available for individual use, and AI-generated content is currently being exploited for malicious purposes, including:

Scams and fraud on social media platforms such as Facebook, YouTube, Twitter, and Instagram
Damaging individuals’ reputations by creating compromising videos or images
Bypassing biometric passwords through image and sound manipulation
Spreading fake news and disinformation
Blackmail and extortion
Identity theft

It’s important to note that not all deepfakes are malicious. For instance, Hollywood uses AI-generated videos in movies to age or de-age actors for their roles.

Here are six tips to protect yourself from the risks associated with deepfakes:

Be Skeptical: Approach sensational or controversial videos or audio clips on social media with a critical mind, especially if the source is not reputable. If something sounds “too good” to be true or the information is sensitive, perform additional checks.
Verify Sources: Verify information from multiple trusted sources before believing or sharing information that could be based on a deepfake. Check text, video, and audio.
Use Technology: Use security tools like Bitdefender’s solutions that can help detect and block phishing attempts and other illicit activities that could leverage deepfakes.
Stay Informed: Educate yourself about the latest developments in deepfake technology, new scams, and the methods used to detect them.
Protect Your Identity: Use services such as Bitdefender Digital Identity Protection to monitor and get alerts if your personal information is used online, which could include the misuse of your likeness in deepfakes. Bitdefender Digital Identity Protection also allows you to detect social media impostors who could use your identity to ruin your reputation or conduct scams in your name.
Report Suspicious Activity: Report any encounter with deepfakes (videos, photos, or audio) or instances of impersonation to the social media platform and authorities such as the Internet Crime Complaint Center (IC3), and local police.

Remember, the more proactive you are in protecting your digital identity and privacy, the less likely you are to fall victim to malicious use of deepfakes and online impersonations.