Can AI Really Detect AI? The Paradox of DeepFake AI Detection

In 2023, a video supposedly of Will Smith eating spaghetti ignited much revulsion and chatter across social media. The distorted and unnatural visuals that some described as "demonic" marked the moment AI-created deepfakes broke into the spotlight. The Will Smith video went viral partly because the immature technology had done such a bad job. But from this crude and unpleasant start, deepfakes have only improved. 

Soon, viewers suspecting a deepfake had to look for less obvious flaws, like six-fingered hands or mismatched lighting. However, we are now at a point where the technology can easily pass the “Will Smith Eating Spaghetti Test” and produce results that are extremely convincing and increasingly difficult to detect without specialized tools. So if AI is now so expert at creating synthetic content, it’s only natural to ask “Can AI detect deepfakes?”. Read on to find out if AI deepfake detection is a viable solution to this strange new world.

Why Do Deepfakes Matter? The Importance of Deepfake Detection

Hand-in hand with the dramatic improvement in quality comes the equally explosive spread of deepfake content. Researchers predict that as much as 90% of online content may be synthetically generated by 2026. And the real-world impacts are mounting, too. In 2024, 50% of companies dealt with deepfake frauds, which on average cost companies around $450,000. So far, in the first half of 2025, $410 million has been lost to deepfake fraud, that’s almost 46 percent of the cumulative deepfake financial losses of $897 million globally.  It’s no surprise that 66% of executives consider deepfakes to be a significant danger to their organizations. 

But perhaps most concerning is the idea of the digital world turning into a contaminated commons. What started as a technological curiosity used for pranks and skits has evolved into a tool for making sophisticated forgeries, distortions of reality that threaten our ability to believe what we see and hear, the very foundation of trust.

As Thomas Hoskin, Director of Product Management at ZeroFox, warns: "For thousands of years, people have done business transactions face-to-face, on the principle that if I can see you, and I can speak to you, I can trust you.”

“The challenge is that in a deepfake world, where face-to-face interaction is often through a video call, that is no longer true," he says.

Finance departments are particularly vulnerable: "Every day, they handle payments; urgent requests come in, and the way that transaction might be verified is by getting a sign-off from the CEO. Seeing his face, hearing his voice, might be a signal of trust to authorize that transaction. Previously that was okay. That is not true anymore," Hoskin says.

When users can't distinguish real from fake, will they stop trusting everything? And when trust breaks, business breaks with it. 

This might seem like a stark picture, but we’re here to bring you practical solutions. When tackling any threat, the first requirement is always to “Know thy enemy”. So, before we look at deepfake detection using AI, let’s investigate the world of deepfakes and how they’re created. 

What is a "Deepfake"?

A deepfake is synthetic media generated or manipulated using artificial intelligence and machine learning techniques to mimic a person using text, audio, image, or video content that appears authentic to the human eye and ear. Bad actors exploit the technology for impersonations, social engineering attacks, and other fraud schemes.  

Deepfake content has become so realistic that 99.9% of consumers in the U.S. and U.K. are unable to identify fake content.

How are Deepfakes Made?

Unlike basic video editing or traditional photo manipulation, deepfakes are the product of advanced AI models that analyze thousands of data points about facial movements, voice patterns, and behavioral characteristics. This use of AI gave rise to the term “deepfake”, a combination of "deep learning" and "fake".  

While creating a deepfake involves sophisticated technology, the process itself is relatively cheap and widely accessible. What once required Hollywood-level resources and expertise can now be accomplished on a personal computer, lowering the barrier to entry for both legitimate creators and malicious actors.

The process typically begins with collecting source material, including photos, videos, or audio recordings of the subject to be impersonated. Modern deepfake generators need surprisingly little data; some voice cloning systems require just 3-5 seconds of audio to achieve 85% accuracy.

The core technology relies on generative adversarial networks (GANs), where two AI systems work against each other: a generator to create fake content and a discriminator that tries to detect forgeries. If the generator fails to fool the discriminator, it tries again with another version. After thousands of iterations, the generator learns to create increasingly convincing fakes that fool the discriminator.

For video deepfakes, a common process involves face-swapping technology that maps facial features, expressions, and movements from one person onto another. Audio deepfakes, used for voice phishing or combined with real video to create cheap fakes, work on similar principles, analyzing speech patterns, tone, and cadence to synthesize new spoken content that matches the target's voice signature.

What is AI Deepfake Detection?

AI deepfake detection describes the use of various artificial intelligence systems, technologies, and methodologies to identify synthetically generated or manipulated media. Deepfake detection using AI is necessary to deal with the quality and quantity of fraudulent media that now far outstrips the human ability to deal with it.

What are the Key Indicators of Deepfake Content?

Even the most advanced deepfakes can leave behind faint digital fingerprints. Experts use visual analysis, audio detection, and behavioral biometrics to distinguish authentic content from AI-generated frauds. Here’s what they look for:

• Visual Analysis:

Deepfake detection using AI systems identifies deepfakes through lighting inconsistencies (shadows and illumination mismatches), geometric errors (incorrect perspective alignment), and face-swap artifacts at blend boundaries. Frame-to-frame jitter in facial movements also points to synthetic manipulation.

• Audio Detection:

Voice clone detection analyzes pitch patterns, breathing rhythms, and speech cadence anomalies that AI struggles to replicate. Systems check mouth-voice synchronization and use voice-print matching against authentic samples when available.

• Behavioral Biometrics:

Individual patterns in blinking, gesturing, and micro-expressions are difficult for AI to reproduce accurately. Detection examines temporal consistency, as real humans show natural variation, while deepfakes often display repetitive or unnaturally consistent movements over time.

How does AI deepfake detection work?

Deepfake Detection with computer vision technology employs sophisticated algorithms to analyze visual data frame-by-frame at a speed and level of detail impossible for human observers. 

Systems called convolutional neural networks (CNNs) are trained on millions of real and synthetic images and learn to identify patterns that distinguish authentic content from deepfakes. They process thousands of frames per second, examining multiple visual layers simultaneously. 

Surface-level analysis identifies obvious artifacts like resolution mismatches, blending errors, unnatural eye movements, or blurring around facial boundaries. 

Deeper analysis uses Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks to find clues that escape single-frame scans. They examine temporal consistency in how features change across frames, and spatial relationships that AI generators often struggle to maintain perfectly.

Meanwhile, audio AI deepfake detection focuses on voice patterns, breathing rhythms, and the synchronization between lip movements and speech. Many detection approaches also search for technical signatures, the mathematical fingerprints left by known AI generation models. 

That’s the theory behind the technological approach, but how well can AI detect deepfakes in the real-world? 

The Challenges of Deepfake Detection Using AI

While useful for specific content, most AI deepfake detection tools can't address the full spectrum of never-ending deepfake threats appearing across the internet.

ZeroFox AI Product Manager Nico Alvear points out that one of the biggest stumbling blocks for deepfake detection using AI is the adversarial techniques used to create them in the first place.

“The nature of the generative adversarial networks (GANs) that produce fakes means that even if you have an excellent deepfake detector today, every failed deepfake provides training data for better models, rendering the detector useless," he says. 

Hoskin agrees: "What we're seeing is an arms race.” 

“Cybersecurity companies try to invent more methods to detect whether a video is fake and threat actors make their videos harder to detect. That really poses a problem because it's not reliable as a detection method."

And technological asymmetry also favors attackers. They can run private models on personal computers, avoiding the signatures and tells of popular generation platforms. This decentralization makes tracking and attribution increasingly difficult. What’s more, they can easily test their models against publicly available detection tools, iterating until they bypass defenses. Where they need only one successful deepfake to cause damage, defenders struggle trying to catch every threat while protecting against both current and unknown future techniques. 

The reality of this never-ending rivalry is that even Monday’s cutting-edge technical detection approaches may be obsolete by Tuesday.

Scale presents another major hurdle. With the financial and technical barriers to deepfake creation all but eliminated, the sheer volume of content requiring analysis overwhelms any traditional threat detection approach. Organizations must somehow monitor across social media, messaging platforms, the broader internet, as well as the deep and dark web, an impossible task without automated, intelligent systems.

Then there’s the contextual challenge. Traditional AI detection approaches, focused purely on identifying synthetic content, face practical limitations in addressing real-world deepfake threats. The binary question "Is this real or fake?" misses crucial context about intent, impact, and risk. Not all synthetic content is malicious, in fact, the technology has many legitimate uses including in entertainment, education, and creative expression. 

Consider the false positive problem. A detection system that flags every piece of synthetic content would overwhelm security teams with alerts about harmless creative content and educational materials. As Alvear notes: "If you build a sophisticated infrastructure that relies solely on AI deepfake detection, you will find vast amounts of content that is not relevant, say, parody videos that are not harmful."

So, effective detection systems must somehow differentiate between benign synthetic content and harmful deepfakes requiring action, a skill that goes well beyond most pure technical AI deepfake detection methods.

Perhaps most importantly, traditional technical detection alone fails to support the human factor. How does AI deepfake detection help you if alerts aren't actionable, response capabilities don't exist, or the broader context of threats isn't understood? 

Breaking this cycle requires a fundamental overhaul in strategy. Rather than striving to improve technical deepfake detection using AI, leading platforms acknowledge that perfect surveillance is impossible, pivoting instead to practical risk mitigation. 

Trust ZeroFox AI Deepfake Detection + Human Expertise

In a reality where deepfake are predicted to cause$40 billion worth of fraud losses in the United States by 2027, the most relevant question to ask is no longer “Can AI detect deepfakes?”, it's whether organizations can go beyond detection to achieve a unified threat defense. 

ZeroFox’s intelligent unified approach identifies high-risk content regardless of whether it's synthetic or authentic, revealing threat campaigns that single-point detection misses and providing resilience against evolving threats. 

Find out more about how ZeroFox safeguards you from abuse, fraud, and attack by removing threats and restoring the truth in a world full of fakes.