How to Detect DeepFakes: A Guide for Security Teams

Did you know that AI needs just one 3-5 second sample of your voice to clone it with 85 percent accuracy? Or that 99.9 percent of people can’t tell a deep fake from the real thing? 

What began as a novelty technology that required specialized expertise is now a reality-bending tool accessible to everyone, cybercriminals included. 

"Very, very quickly, over the course of the last 12 or 18 months, the tech has gone from generating images and video with obvious flaws, to producing content that is remarkably subtle,” says Thomas Hoskin, Director of Product Management at ZeroFox.

“In the next year or so, I think the majority of people won't be able to tell whether something is malicious or fraudulent without some kind of detection solution to help them.”

But what should that deepfake threat detection look like? Read on to learn how to detect deepfakes and discover the most effective deepfake detection tools that help security teams respond to and protect against these destructive new threats.

Deepfake Threat Detection 101

The key to tackling any complex threat is simple: know thy enemy. In the accelerating arms race between deepfake creators and defenders, those who fully understand the technology control the battlefield. 

So, let’s break down what deepfakes really are, how malicious actors exploit them against organizations, and why conventional security beliefs fall apart when deepfake content comes into play. 

What is a Deepfake?

Deepfakes are synthetic media created using artificial intelligence deep learning algorithms to swap a person's appearance or voice with someone else's. Neural networks trained on thousands of images, videos, or audio samples generate convincing but manipulated or entirely fabricated content.

How are Deepfakes Made?

Deepfakes are made using generative adversarial networks (GANs) in which two AI systems compete against each other, one creating increasingly convincing fakes while the other attempts to detect them. This adversarial training produces eventually outputs that can deceive the detector and even trained observers.

To make life-like deepfakes, creators need several key ingredients: audio, video, or image-based training data of the target individual, powerful computing resources, and sophisticated AI models, all of which are within easy reach of bad actors. 

Modern deepfake creators can work with brief audio samples for voice phishing (aka vishing) or cheapfakes that pair genuine video with fabricated speech. 

Simple image or video face swaps can be done using just a handful of photos or a few hundred video frames. The process involves facial mapping, expression transfer, and seamless blending techniques to match lighting, skin tone, and environmental factors. 

Detailed, professional deepfakes demand hundreds to thousands of high-quality images or video frames from diverse sources. But again, with so much content freely available on social media, YouTube, and corporate websites, the raw material is not difficult to find.

Types of Deepfakes

Cybercriminals use a variety of approaches, ranging from making basic audio edits of authentic videos to lifelike digital puppets of real people. Each type of deepfake poses particular hurdles for detection and prevention. Here’s the complete spectrum of risks you might encounter:

• Cheapfakes: This is the most straightforward type, which merges genuine video clips sourced from public platforms with altered audio. Criminals often extract real videos from LinkedIn, YouTube, or conferences and overlay synthetic speech, making detection difficult since the video itself is genuine.
• Audio Cloning: Needing just a few minutes of audio samples, this technology is often misused in phone scams to impersonate company leaders or trusted individuals, making it difficult for victims to spot the deception.
• Image Manipulation: Photographs that are either created or modified by AI, often used to craft fake identities, falsify documents, or enable identity theft.
• Synthetic Text: AI-generated written content that imitates specific writing styles, enabling highly targeted phishing campaigns that match the tone and patterns of trusted contacts.
• Hybrid Deepfakes: These are moderately advanced attacks that mix real and fake components, like using real photos paired with fake audio, editing genuine video clips, or swapping faces while syncing mouth movements to match.
• Full Deepfakes: Entirely synthetic, with both the video and audio created from the ground up, resulting in realistic but completely false content that doesn't match any source material.
• Puppet Master Attacks: This is the most sophisticated form, where large amounts of video footage are analyzed by AI to produce interactive digital copies that can convincingly impersonate individuals in real-time during live video calls, taking advantage of our natural trust in face-to-face communication.

"There is a lot of subtlety and gray area in the content being published,” Hoskin says.

“Sometimes the visual video content is legitimate, but the audio is fake. Other times, you might have real audio and a real image, but deceptively edited to change the original message being communicated. But primarily, the big risk we see targeting people is fake video plus fake audio."

Are Deepfakes Really a Threat?

With 62 percent of organizations facing deepfake attacks over the past year, the question seems to be no longer if your organization will be hit by a deepfake, but when. To date, losses from deepfake fraud have totalled $1.56 billion, over $1 billion of which was lost in 2025 alone. Compare that to the $530 million lost over the previous five years put together, and it’s easy to see deepfakes are a rapidly escalating menace. If these figures seem dramatic, consider that they may be serious underestimates of the scale of the problem. Haywood Talcove, CEO of LexisNexis Risk Solutions' government division, put the worldwide cost of deepfake fraud at one trillion US dollars in 2024.

Who is Affected by Deepfakes?

The fallout for individual businesses can also be enormous. While the average cost of a deepfake attack has reached around $450,000, some companies don’t get off so lightly. Arup, a leading design and engineering consultancy, lost around $25 million to fraudsters who used digitally cloned executives to order wire transfers. 

"Any company generating a reasonable amount of revenue is a target for these threat actors,” Hoskin says.

“If you have customers or business partners that trust you, and those business partners or customers are giving you money or interacting with you, that is enough to be a target for a deepfake threat actor."

The type of exposure varies by industry, public profile, and digital footprint. For example, financial services, government contractors, and publicly traded companies face particular vulnerabilities thanks to the potential for market manipulation and regulatory consequences. But, across the board, highly visible executives, public-facing brands, and organizations involved in sensitive transactions face greater risks. 

Rank-and-file employees are also potential targets for spear-phishing campaigns using voice synthesis or video manipulation. 

"Remote work environments amplify these vulnerabilities,” warns Nico Alvear, Product Manager of AI at ZeroFox.

“As distributed teams rely more heavily on digital communication channels, they become increasingly susceptible to synthetic media attacks.”

How Deepfakes Erode Trust: What are Deepfakes Used for?

The scope of applications for deepfakes is growing every day, but threat actors generally weaponize the technology in the following ways:

• Non-consensual explicit content (32%) — The highest reported use, targeting individuals for harassment, extortion, and reputational damage
• Financial fraud (23%) — Social engineering, impersonating executives, family members, or trusted contacts to authorize fraudulent transactions, bypass verification procedures, and manipulate victims into transferring funds
• Political manipulation (14%) — Targeting elections, public opinion, and democratic processes with false statements attributed to political figures
• Misinformation and disinformation (13%) — Spreading false narratives via convincing but entirely manufactured video evidence
• Identity theft (10%) — Using voice synthesis to bypass authentication systems and take over accounts

At the most basic level, deepfakes use technology to exploit human psychology, targeting our ability to trust each other. And when trust breaks, business suffers. 

"For thousands of years, people have done business transactions face-to-face on the basis of: 'I can see you, I can speak to you, therefore I can trust you',” Hoskin points out.

"But now, relying on seeing a person or hearing them speak, saying, 'I need you to pay this amount of money to this company on this day,' is risky because it is no longer an indicator that that person has actually authorized that action."

Are Deepfakes Illegal?

The laws regarding deepfakes are still trying to catch up with the technology. Recent legislation, including federal measures that make it illegal to create explicit content without consent and that require fast action to take down such content, show that the government is increasingly grappling with this issue. But even without new laws, companies can be held liable for not identifying deepfakes and dealing with any related fraud, harassment, or defamation that involves their personnel or their brand.

How to Detect Deepfakes: Common Detection Methods 

Security teams can employ various strategies, from hunting for things that just don’t smell right to using advanced technology to sift through vast amounts of data at once. Each approach brings unique strengths to the table as threat actors continue refining their techniques. Here’s how to progress from hoping you'll spot the fakes to knowing you have the tools to catch them:

7  Manual Recognition Techniques for Identifying Deepfakes 

Careful visual and auditory scrutiny often exposes flaws in synthetic media. The key lies in taking a step by step approach to zero in on the specific flaws that today's AI models struggle to get right.

How to detect deepfakes with key visual markers:

1. Facial details: Watch out for unnatural skin, either too smooth or showing mismatched tones. Look closely at boundary areas where the face meets the background, checking for blurring, flickering, or other edge problems.
2. Eyes and blinking: Look for odd blinking patterns, unnatural eye shine, empty looks, or eyes that don't line up right, all signs of digital tampering.
3. Mouth and lip sync: Check whether lip movements line up naturally with speech. Watch out for odd mouth shapes, missing detail inside the mouth, or teeth that blur together.
4. Lighting and shadows: Make sure shadows and bright spots align with the setting. Shadow directions that don't match up, wrong lighting intensity, or unnatural shine on skin and glasses often give away fakes.
5. Movement and micro-expressions: Real human movement has micro-expressions, small twitches and tiny face movements we don't think about. Deepfakes often show unnaturally smooth or jerky movements, stiff body language, and lack the small muscle shifts that mark real footage
6. Hands and accessories: Hands still trip up AI, so watch for warped fingers or unnatural finger counts. The accessories people wear might shift around unnaturally or look lopsided.
7. Audio anomalies: Listen for tone problems, missing background noise, breathing that's off, or sound that doesn't match what you're seeing.

How to Detect Deepfakes Using Behavioral and Contextual Analysis

Another approach to identifying deepfakes involves analyzing the bigger picture and picking up on the behavioral patterns that clever fraudsters use to manipulate trust and emotions. This involves: 

• Evaluating sources: Look into the trustworthiness and background of accounts sharing content. New or nameless accounts pushing shocking claims need extra checking. Do responses seem scripted or evasive? Are they making requests to move to different communication channels?
• Fact-checking and cross-referencing: Cross-check big claims with well-known news sources and fact-checking groups like Snopes, Reuters, or PolitiFact before taking high-stakes content at face value.
• Reverse image searches: Use reverse image search tools like Google Images or TinEye to track down where content came from and spot potential recycling or tampering.
• Persona analysis: Make sure writing styles, knowledge levels, or follower counts line up with known patterns of the person the messages are supposed to be coming from. Are they reluctant to discuss unexpected topics or personal details?
• Emotional manipulation warnings: Content designed to set off strong feelings right away, such as anger, fear, or excitement, needs careful checking before sharing or responding. Are they pressuring someone to make immediate decisions?

Technical, Automated, and AI-Powered Deepfake Detection Tools

As deepfake technology gets smarter, manual identification is no longer enough to guarantee even basic levels of protection. Today's distorted reality demands AI-powered platforms to tackle the immense volumes of suspicious content and attempt to keep up with the sneakiest new deepfake techniques.

These modern detection solutions bring together machine learning, computer vision, and forensic checking to comb through thousands of data points at once, finding patterns people can't see while always improving at fighting new threats:

• Computer Vision Algorithms: Smart neural networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), pick out pixel-level oddities, unnatural movements, and lighting discrepancies. These models look at skin textures, facial boundaries, shadow line-ups, and implausible body movements like blinking and lip matching to flag potential fakes.
• Edge and Blending Artifact Analysis: Automated systems zero in on frame boundaries and face blend areas, flagging subtle pixel glitches or edge distortions introduced during synthetic merging.​
• Temporal and Audio Analysis: RNNs, transformers, and sequence models track timing patterns in speech-to-lip match-ups, natural motion flow, and rhythm steadiness. For audio, spectral fingerprinting looks for robotic artifacts, pacing problems, mismatched breaths, and background irregularities.
• Anomaly Detection and Confidence Scoring: Some platforms use machine learning to assign manipulation risk scores, helping teams focus on looking into suspicious content while keeping work flowing smoothly.
• Forensic Metadata Examination: Automated checking of embedded media metadata, like EXIF tags, timestamps, GPS records, or device IDs, shows tampering via inconsistent origins, fishy edit histories, or compression indicators that don't fit with original capture methods. Such tools help verify or discredit claims of where and how a media file was made.​​
• Cross-Modal and Ensemble Approaches: Leading platforms combine video, audio, and metadata checking at the same time. By comparing facial details to acoustic features or matching scene lighting to file metadata, these systems improve accuracy and provide more powerful protection against elaborate fakes.​​
• Continuous AI Adaptation: Detection works well only with ongoing model retraining and updates that incorporate new attack types. Transfer learning lets systems quickly recognize new deepfake techniques as they show up.

Challenges in Identifying Deepfakes

While technological tools form a sturdy foundation for a deepfake detection solution that is scalable, adaptive, and precise, the nature of deepfakes means a purely technical approach faces multiple hurdles as threat actors actively adapt their methods. 

Here’s what you need to know when looking for a deepfake detection solution:

Trade-offs of Scale Versus Precision 

Organizations must weigh precise detection against what's operationally efficient. Manual review provides nuanced analysis but can't scale up to monitor enormous volumes of content across social platforms, the wider internet, and the deep and dark web. Automated systems can handle that volume by themselves, but are prone to throwing up false positives that can overwhelm security teams.

Not all fake media represents a security threat. Entertainment, educational content, and legitimate business uses of AI-generated media spread alongside harmful applications. 

This challenge only gets worse as more and more legitimate companies integrate AI into their content creation tools. Separating malicious deepfakes from harmless filtered videos, enhanced conference calls, or entertainment content requires careful consideration. Security teams must size up not just whether something is real but also interpret its potential for harm, judgments that need human reasoning beyond what purely technical solutions can provide.

Teams must also have practical response thresholds. Which tampered content calls for immediate action versus continued monitoring? How should resources be prioritized across varying threat levels? These decisions need expertise to balance technical indicators, contextual factors, and business impact.

The Arms Race for Deepfake Detection Tools 

With new generation techniques emerging monthly, deepfake detection methods that work against today's threats may fall short against next-generation approaches. 

"What we’re seeing at the minute is an arms race,” Hoskin says. 

“As cybersecurity companies invent more and more methods to detect whether something is fake, we see threat actors making their content harder to detect in terms of the known signals and characteristics that indicate they are fake."

Alvear agrees, explaining that the adversarial nature of GANs means that, “As soon as you build an AI detector, it will be used to train better generators.”

Attackers make good use of this asymmetry, testing their latest innovations against publicly available detection tools until they slip through successfully. 

Custom AI model development makes detection harder still. Threat actors make their own generation systems with unique traits, avoiding signatures linked to common platforms. 

“In my opinion, that war—the war of generating AI detectors—is lost,” Alvear says. 

"And detecting whether content has been synthetically manipulated or is a deepfake is not the full story. That detection alone does not mean the content is malicious or going to damage anybody."

“So, it makes more sense to focus on what the content is about."

Attribution and Response Limitations

Deepfake threat detection is only the first step. Finding out who made it, understanding why, and taking action against it can prove just as difficult without the right solution. Anonymous distribution platforms, encrypted communications, and international jurisdictional issues make tracking down and dealing with bad actors a challenge.

Even when deepfake origins are confirmed, tracked, and traced, detection alone gives few options. Content spreads quickly across platforms, making full removal a seemingly impossible game of whack-a-mole. Legal fixes take time, while reputational damage continues to mount. 

The Most Advanced Deepfake Threat Detection

These challenges mean that, instead of fighting yesterday’s war using purely technical detection methods, you need unified response strategies that offer speedy discovery, instant validation, and rapid damage control. Smart deepfake defense systems fuse multiple detection methods with the additional capabilities needed to turn discovery into disruption:

• Unified Protection

ZeroFox gives you complete visibility across social media platforms, ecommerce sites, the open internet and the deep and dark web. That means:

• 4 million unique assets monitored daily

• 65 million+ domains and URLs scanned annually

• 1,000 dark web forums continuously watched

• 10k+ brands protected across industries

• 21,000 executives/vips protected

By mapping digital footprints, monitoring for impersonation attempts, and maintaining contextual awareness of organizational threats, security teams can identify and neutralize deepfake campaigns before significant damage occurs.

“We collect all content being posted, to help you find things relevant to your business,” Hoskin says.

“More specifically, we help you find things which are a risk or a threat because they are fake, malicious, or targeting your business or your customers."

• Multimodal Analysis

Visual analysis alone will miss audio anomalies, while speech verification can’t account for visual manipulation. So, effective detection looks at multiple content dimensions, analyzing facial expressions, voice patterns, body language, textual content, metadata, and behavioral indicators simultaneously.

The ZeroFox platform inspects:

• Visual: face swap artifacts, lighting inconsistencies, frame-level jitter
• Audio: voice-clone markers, rhythm anomalies, mouth-voice mismatch
• Text & intent: fraud lures, credential harvest flows, market-moving claims
• Network & behavior: coordinated posting, look-alike domains, API abuse, wallet reuse

This multimodal approach reveals deepfake signals such as inconsistencies in emotional expressions between voice and face, contradictory body language and verbal messages, or metadata incompatible with the claimed origin of the content.

• Integrated Human and Machine Intelligence

In addition to technological efficiency, security teams also need workflows that take advantage of the strengths of human know-how to detect, validate, and disrupt deepfake threats. 

Automated systems can perform initial screening and flag suspicious content, while human analysts evaluate context, assess intent, and make final determinations for prioritization. This hybrid approach maximizes both speed and accuracy within real-world resource limits, enabling security teams to focus on legitimate risks rather than chasing alerts and false positives. 

Capabilities include:

• Natural language processing and semantic analysis to understand the nuance and context of messages and content.
• Behavioral analysis to identify actions inconsistent with known patterns of individuals.
• Cultural and situational awareness to recognizes implausible scenarios that technical analysis might miss.

• Context and Intent Evaluation

"Generative AI is not the problem; the problem is the malicious intent,” says Alvear.

“That’s why our approach is built on detecting relevant threats, not whether something is made by AI, it’s a more comprehensive solution."

Hoskin explains how ZeroFox takes a very different, outside-the-box approach:

“We look at the content and determine the intent of the person who made it,” he says.

“We use transcription techniques and image recognition techniques to detect essentially two things. Number one: Do we think it is relevant to our customer? Is it using their imagery? Is it using things that we think represent executives of that company?”

“Number two: What is the risk to the business? We can look at things like an audio transcript, the message within a video, perhaps words presented on the screen, to identify and decide whether the message the content is conveying is high-risk."

“If we think it is a high risk, then we will let our clients know about it.”

• Rapid Takedowns

Speed of discovery, validation, and disruption is a major advantage when tackling deepfakes.

"When something has already gone viral, it’s everywhere and very hard to stop,” Alvear says.

"The best way to prepare is to be proactive, not reactive. Don't wait until a customer or client phones you up, saying, 'I just received a message from your CEO that suggested I do this, and I've done it'. Get the right tools and processes in place," advises Hoskin.

“But intelligence without action is just trivia. We're not here to observe, we're here to end threats." 

“So, we work with a whole range of partners to get content taken down as quickly as possible and at scale."

ZeroFox can move from identification to validated takedown with fewer hops because the detection process already links visual, audio, text, and network indicators to an actor and their infrastructure.

This means the most dangerous content is flagged for immediate take down across ZeroFox's Global Disruption Network of hosts, registrars, ISPs, and platforms, which blocks and removes malicious content within minutes.

ZeroFox takedowns are by backed by 50 patents enabling over 1 million takedowns annually with a 98% success rate for VIP, brand, and domain cases. 

Protect Your Business From Deepfakes

As long as deepfake generators keep improving, the fight between bad actors and defenders will continue apace. The goal shouldn’t be finding every possible deepfake but staying aware of what's happening, responding quickly to real threats, and keeping stakeholder trust even as threats grow more complex. Organizations that accept that this fact and deploy flexible, multi-layered defenses paired with human expertise will keep themselves ahead of the game.

Find out more about how ZeroFox doesn't just tell you what's wrong—it takes it down, so you can bring back trust in an age of deepfake media.