What is a Deepfake Attack?

Bart Lenaerts-Bergmans - April 16, 2024

What are deepfakes?

Rapid and remarkable advancements in AI technology have paved the way for groundbreaking developments in recent years. Though most innovations have been positive, the rise of AI has also led to the emergence of deepfakes. Deepfakes are AI-generated forgeries — false images, audio, or video — that appear convincingly genuine.

As AI technologies become more accessible and advanced, deepfakes will appear increasingly convincing. The challenge of distinguishing authentic content from fabrication will grow in complexity, and this poses significant risk to information integrity.

In this post, we’ll explore the concept of deepfakes, looking at how they have come about and the impact they have on modern society. Then, we’ll consider how enterprises can combat this malicious use of AI with security tools and proactive measures.

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Understanding deepfakes

Though deepfakes stand as a testament to the leaps in artificial intelligence and machine learning, they also pose a formidable threat to the integrity of information in modern society. These digitally altered likenesses have the potential to reshape public opinion, damage reputations, and even sway political landscapes. In this expanded section, we will explore the mechanics and implications of deepfakes.

The technology behind deepfakes

Generative adversarial networks (GANs) offer one example of deepfakes. A GAN can be thought of as two AI systems in a digital tug-of-war. One AI — the generator — tries to generate fake content that looks real. The other — the discriminator — judges whether the content is real or fake. As the process iterates countlessly, each round makes the fake content more and more difficult to distinguish.

GANs, an increasingly advanced and available technology, enable the creation of videos and audio that can fool even the keenest eyes and ears.

How deepfakes are created

Creating a deepfake is not as simple as applying a filter on a photo app; it’s a complex process requiring vast amounts of data and computing power.

First, you need a dataset of images, audio samples, or videos of the person you want to mimic. The more data, the better. Then, using GANs, the AI learns how to replicate the person’s voice, appearance, and mannerisms.

Finally, this learned model is applied to generate new content, where the person appears to say or do things they never actually did.

Types of deepfakes

Deepfakes can be broadly categorized into video and audio forgeries. Video deepfakes involve altering a person’s face or body to look like someone else, and they are often used in celebrity face swaps or political misinformation.

Audio deepfakes mimic someone’s voice, allowing a person to be convincingly replicated saying things they’ve never said. Both types of deepfakes have their use cases, ranging from playful entertainment to more sinister purposes like fraud or political manipulation.

Real-world examples of deepfakes

Some deepfakes that have garnered a lot of attention have involved celebrities and public figures:

Though the above examples showcased the technological prowess behind deepfakes, they also highlighted their potential to mislead and manipulate public perception.

Innovation or means of deception?

Deepfakes are at a crossroads between being a remarkable technological breakthrough and acting as a tool for deception. On one hand, they offer exciting possibilities for the creative industries, such as filmmaking, gaming, and virtual reality. On the other hand, their potential for abuse in spreading misinformation or malicious content cannot be ignored.

The impact of deepfakes on society

Deepfakes pose significant challenges to the fabric of truth and trust within society. Their capacity to create convincing false narratives has amplified concerns over disinformation, particularly in sensitive areas like politics and public opinion. Deepfakes can be used to harm reputations, manipulate public sentiment, sway elections, and erode democratic processes.

Beyond politics, deepfakes present a threat to personal security. The technology can be used for malicious purposes, such as blackmail, fraud, and cyberbullying.

As a result, the need for vigilance and sophisticated detection methods has never been more critical.

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Detecting and mitigating the malicious uses of deepfakes

Combating the malicious uses of deepfakes is a daunting challenge and a constant battle in the realm of cybersecurity. AI technology is advancing rapidly. As soon as new detection methods are developed, the technology used to create deepfakes evolves, making deepfakes more sophisticated and harder to identify.

This ongoing technological arms race against dark AI — the use of AI for malicious purposes — underscores the complexity of the problem. Cybersecurity platforms, detection tools, and protective measures must continually adapt and improve to keep pace with the increasingly advanced AI leveraged by malicious actors.

In response, the cybersecurity community has been developing a variety of tools and strategies aimed at identifying and neutralizing deepfakes. These include:

  • AI-based detectors that analyze videos and audio for inconsistencies
  • Digital forensics techniques that scrutinize the integrity of digital content
  • Blockchain technology to verify authenticity through the use of immutable, digital watermarks
  • Identity protection tools to help safeguard individuals’ digital personas from being exploited in deepfake attacks.

Despite these efforts, the threat landscape remains challenging. There is no foolproof and lasting solution in sight. Instead, today’s organizations and end users must focus on resilience, awareness, and the constant evolution of defense mechanisms to mitigate the risks posed by deepfakes.

Defending against dark AI with the CrowdStrike Falcon platform

In this article, we’ve explored the emergence and growing presence of deepfakes. We’ve considered their impact on society, highlighting the dual nature of this technology as both a marvel of AI innovation and a tool for deception. Then, we highlighted the challenges in detecting and mitigating malicious uses of deepfakes. Combating dark AI is undoubtedly complex.

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Bart is Senior Product Marketing Manager of Threat Intelligence at CrowdStrike and holds +20 years of experience in threat monitoring, detection and intelligence. After starting his career as a network security operations analyst at a Belgian financial organization, Bart moved to the US East Coast to join multiple cybersecurity companies including 3Com/Tippingpoint, RSA Security, Symantec, McAfee, Venafi and FireEye-Mandiant, holding both product management, as well as product marketing roles.