How to Detect and Prevent Deepfake Threats: Best Practices from Onix

Imagine receiving a call from your CEO, asking for an urgent wire transfer. You hear the familiar voice, the same tone, and intonation, but here’s the catch—it wasn’t your CEO at all. It was a deepfake.

 

This isn’t science fiction; this is happening right now. Deepfake technology, powered by Generative AI, has crossed a threshold where it can convincingly mimic human voices, faces, and even behaviors. And it’s not just a personal privacy issue—it’s becoming a critical threat to businesses worldwide.

 

At Onix, we’ve seen the risks firsthand, but we’ve also built a suite of defenses designed to help companies stay one step ahead of these increasingly sophisticated attacks. Let’s dive into the world of deepfakes and, more importantly, how you can protect your business.

 

What Are Deepfakes, and Why Should You Care?

Deepfake technology relies on Generative Adversarial Networks (GANs), a type of machine learning model that pits two neural networks against each other: a generator and a discriminator.

 

The generator creates fake media, while the discriminator evaluates its authenticity. Over time, the generator becomes better at producing realistic fakes, while the discriminator becomes more adept at spotting flaws.

 

Key Steps in Deepfake Creation:

 

 

The result? Deepfakes are so convincing that they can bypass biometric security systems and fool human viewers.

 

For instance, imagine an audio file that replicates a CEO’s voice down to the intonation, pauses, and background noise. Now, picture that voice used in a spear-phishing attack to approve fraudulent wire transfers or give orders to employees.

 

Real-World Example: In 2020, a UK-based energy company CEO was convinced to transfer $243,000 to a Hungarian supplier, believing he was speaking with his boss on the phone. But the voice was a deepfake, and the money was gone before anyone realized what had happened. This demonstrates the very real financial stakes tied to deepfake technology.

 

Biometric Authentication Under Siege: Why It's No Longer Enough

Biometric authentication systems, once considered highly secure, are now vulnerable to sophisticated attacks fueled by deepfakes. From voice-based login systems to facial recognition, hackers are using GANs to mimic the unique biometric signatures of individuals and bypass authentication barriers.

 

How Deepfakes Can Exploit Biometric Systems

Voice Authentication

Deepfake models can replicate a target’s voice pattern by analyzing publicly available voice data (e.g., speeches, interviews). Once the model is trained, attackers can use it to gain unauthorized access to voice-secured accounts.

 

Example: In one incident, researchers successfully tricked a bank's voice authentication system using an AI-generated voice, granting access to secure accounts.

Facial Recognition

Deepfake videos can be used to fool facial recognition systems by creating videos that simulate natural human movements and facial expressions.

 

Example: Attackers used deepfake videos to bypass video-based identity verification systems (eKYC), enabling them to impersonate individuals during online banking transactions.

 

Biometric systems rely on pattern recognition to authenticate users. For voice, it's the nuances in speech, tone, and rhythm. For video, it's facial landmarks, expressions, and even micro-movements. Deepfakes, however, mimic these nuances so effectively that the lines between real and fake blur.

 

How Do Deepfakes Affect Businesses?

At Onix, we understand how this technology works on a technical level, but more importantly, we see how it can undermine business operations in the real world.

Let’s break it down:

 

1. Financial Fraud

 

• Criminals can use deepfake technology to impersonate executives, leading to unauthorized wire transfers or fraudulent approvals. The UK CEO example mentioned above is just one of many incidents.

 

2. Reputation Damage

 

• Imagine a deepfake video of your company’s CEO making offensive remarks. Even though the video is fake, the damage to your brand’s reputation could be very real. In an era where videos go viral in minutes, the fallout could be immediate and severe.

 

3. Biometric Security Bypasses

 

• Many businesses are adopting voice and facial recognition as security measures, thinking they are foolproof. But deepfakes can replicate these biometric signatures, allowing bad actors to bypass these systems.

 

4. Stock Market Manipulation

 

• Deepfakes can even impact markets. A recent fake image of an explosion at the Pentagon spread online, causing the S&P 500 to drop 30 points before the hoax was debunked.

 

Here are some more examples of how Deepfake threatens businesses. 

 

 

These are just a few examples of the real-world implications of deepfake technology. The question is: How do you indicate them and protect your business? Let’s go on. 

 

Spotting Deepfakes: Key Technical Indicators

While deepfake technology has made significant strides in recent years, it's still not flawless. Even the most sophisticated deepfakes often contain subtle, telltale signs of manipulation.

 

Identifying these signs is crucial for businesses that rely on media authenticity, especially when deepfakes can be used for fraud, impersonation, or misinformation. Below are some technical indicators that can reveal the presence of deepfake media:

 

Audio Indicators

Detecting deepfake audio can be tricky, but there are several subtle cues that can give it away:

 

1. Inconsistent Background Noise or Ambiance:

Deepfake audio often lacks the natural background noise present in real-world recordings. You might hear a person’s voice clearly, but environmental sounds like background chatter, wind, or echo are unnaturally absent.

 

Additionally, when background noise is present, it may not align with the person's voice, creating an uncanny, "off" feeling.

 

2. Unnatural Intonation or Pauses:

While deepfakes can replicate the overall tone and rhythm of a person’s voice, they often struggle with nuances in speech like natural pauses, stumbles, or emotional fluctuations. A deepfake voice may sound too consistent, with pauses happening at odd moments, or lack the subtle inflections typical of human speech.

 

3. Robotic or Stiff Speech Patterns:

Deepfake voices can sound stiff or robotic, especially during complex sentences. The AI might struggle with specific linguistic quirks, leading to mechanical transitions between words or unnatural phrasing. This can make the speech sound rehearsed or slightly unnatural, like an automated system.

 

4. Spectrogram Analysis:

Advanced detection tools can analyze the audio spectrogram—a visual representation of the frequency spectrum over time. Human voices have irregularities that naturally occur in speech, but deepfake audio lacks these intricacies.

 

Forensic Audio Analysis can spot these anomalies, revealing subtle signs of tampering in the pitch, tone, and rhythm of the audio.

 

Video Indicators

When it comes to deepfake video, even the best fakes are often flawed in ways that can be detected if you know where to look:

 

1. Lip-Sync Errors:

One of the more common giveaways in deepfake videos is the mismatch between the person’s lips and their words. Deepfake algorithms often struggle with perfect synchronization, especially when dealing with complex words or rapid speech. Look for instances where the movement of the lips doesn’t align with the audio, as this is often a strong indicator of manipulation.

 

2. Blurry Facial Features or Skin Inconsistencies:

The facial features in deepfake videos may not be as sharp as the surrounding environment, resulting in blurry or soft-edged visuals.

 

Deepfakes also have trouble replicating realistic skin textures, often leading to overly smooth or "plastic" looking skin that lacks the natural pores, wrinkles, and subtle imperfections of a real face. The lighting on the face might also appear unnatural, as deepfakes can struggle with how light interacts with skin.

 

3. Irregular Blinking or Strange Facial Expressions:

Eyes are notoriously hard to fake. Early deepfakes often produced characters that didn’t blink at all. While blinking has improved in newer models, irregular or unnatural blinking patterns (e.g., blinking too fast, too slow, or out of sync with facial expressions) can still be a giveaway.

 

Additionally, facial expressions may look stiff or lack the fluidity and subtleties that come with real human emotion, like micro-expressions around the eyes or mouth.

 

4. Inconsistent Lighting and Shadows:

In a real-world environment, lighting and shadows follow consistent patterns. Deepfakes, however, often struggle to replicate the way light interacts with different parts of a face or body.

 

You might notice shadows appearing in places they shouldn’t be, or light reflecting oddly on the face. For example, if the video shows multiple light sources, the shadows on the face might not line up with the background, signaling manipulation.

 

5. Inconsistent Clothing and Background Details:

Deepfake technology tends to focus on recreating faces and voices, but struggles with peripheral details. Clothing may not move naturally with the person, and background elements like reflections or objects might look out of place or even distorted. Deepfakes often fail to replicate the fine movements of clothes, like the way fabric folds or interacts with the body.

 

 

Detection Tools: Using AI to Spot Deepfakes

While these manual detection methods can help identify fake content, deepfake detection tools provide a more accurate way to analyze media. Forensic Video Analysis and Spectrogram Analysis are two critical methods used to uncover these subtle discrepancies:

 

• Forensic Video Analysis: This method uses advanced algorithms to detect visual anomalies in deepfake videos. By closely examining the way light interacts with the face or how certain elements, like skin texture and facial expressions, behave under dynamic conditions, this tool can reveal inconsistencies invisible to the human eye.

 

• Spectrogram Analysis: As mentioned earlier, spectrograms break down audio into a visual format, where deepfake voices often fail to mimic the natural irregularities present in authentic human speech. These tools help in detecting unnatural sound waves or repetitive audio patterns that aren’t typical in a real voice recording.

 

Real-World Example: Deepfake Celebrity Videos

A now-famous example involved a deepfake video of a celebrity with an oddly smooth face and awkward facial expressions. The video went viral, but upon closer inspection, experts noted that the skin lacked natural texture, and the facial movements seemed slightly "off"—especially around the eyes and mouth. These small tells led to the video being flagged as a deepfake.

 

Tools like Forensic Video Analysis played a crucial role in identifying the subtle discrepancies in how light reflected off the skin, which was a key factor in revealing the manipulation. The analysis also showed awkward blinking patterns, which further confirmed that the video was fake.

 

The Growing Impact on Businesses: From Financial Fraud to Reputational Damage

The rise of deepfake technology isn’t just an isolated cybersecurity issue; it has wide-ranging implications for businesses across industries. Here are some ways deepfakes are impacting corporate security and operations:

 

 

The damage goes beyond financial losses. The trust between businesses and their customers is at stake. A single deepfake video can severely damage a brand's reputation or manipulate stock prices in the blink of an eye.

 

How Onix Can Help: Defending Against the Deepfake Threat

At Onix, we recognize that deepfake technology is advancing, but so are the defenses against it. Here’s how we help businesses stay one step ahead:

 

1. AI-Powered Deepfake Detection Systems

We use advanced AI tools to detect deepfakes by analyzing subtle patterns that humans can’t easily spot.

 

For example, Photo Plethysmography (PPG) can detect real-time changes in blood volume in videos, revealing whether the video is authentic or fabricated. Similarly, Audio Forensics tools analyze the spectrogram of voice recordings to identify synthetic tampering.

 

2. Layered Security: Multi-Factor Authentication (MFA)

Businesses should not rely on single-factor biometric authentication like voice or facial recognition. Instead, integrating MFA—including passwords, PINs, and token-based systems—adds layers of security that are much harder to breach, even with deepfakes.

 

3. Behavioral Biometrics

At Onix, we encourage the use of behavioral biometrics—tracking how users interact with devices (typing speed, swipe patterns, etc.)—to create a unique profile that is harder for deepfake technology to mimic. If the system detects abnormalities in behavior, it can trigger additional security measures.

 

4. Employee Training & Awareness

Technology alone isn’t enough. Regular cybersecurity training sessions can arm employees with the knowledge to recognize and respond to deepfake-based phishing attacks. We’ve seen first-hand how companies have avoided breaches simply by raising awareness.

 

 

Conclusion: Stay Ahead of the Deepfake Threat

In the face of the rising deepfake menace, businesses must be proactive rather than reactive. By investing in cutting-edge AI detection tools, strengthening authentication systems, and educating employees, companies can greatly reduce their exposure to these sophisticated threats.

 

At Onix, we specialize in helping businesses navigate this evolving threat landscape. Our goal is to fortify your digital security and protect your business from the unique challenges posed by deepfake technology.

 

Let’s collaborate on making your systems more resilient against deepfake threats.

 

FAQ

 

1. What are deepfakes, and how are they made?

Deepfakes are AI-generated videos, images, or audio designed to mimic real people. They’re created using Generative Adversarial Networks (GANs), where one AI model (the generator) creates fake content, and another (the discriminator) evaluates its realism. Over time, the generator improves by learning from its mistakes, producing highly convincing fakes.

 

2. Why are deepfakes a threat to businesses?

Deepfakes pose a major risk to businesses because they can be used in fraudulent schemes like impersonating executives to authorize financial transfers, spread misinformation about a company, or even gain unauthorized access through biometric systems. They can lead to financial loss, reputational damage, and security breaches, especially in social engineering attacks.

 

3. Can deepfakes bypass biometric security systems?

Yes, deepfakes can replicate voices and facial features well enough to trick biometric systems. For example, voice deepfakes can imitate a person’s speech patterns, making it possible to bypass voice authentication systems, while fake videos can fool facial recognition, making biometric security less reliable if not combined with other safeguards.

 

4. How can you detect deepfakes?

Deepfakes can be detected by watching for unnatural features, such as poorly synchronized lip movements, robotic or stiff speech patterns, and inconsistencies in facial expressions or lighting.

 

Additionally, AI-powered tools like Forensic Video Analysis and Spectrogram Analysis can spot subtle irregularities in audio frequencies and video textures that indicate manipulation.

 

5. What can businesses do to protect against deepfake threats?

Businesses can implement multi-factor authentication (MFA) that combines biometric data with other forms of verification like passwords or PINs. They should also use AI-based detection tools, train employees to recognize deepfakes, and continuously monitor media for suspicious activity. Staying updated on evolving security measures is essential to mitigate these risks.