What is Deepfake Fraud Prevention & Detection?

One call, millions of dollars gone. It’s the reality every organization faces as attackers dial up the sophistication and take aim at employees with deepfakes.

Imagine this: Your CEO joins a call with the finance team, instructing the staff to wire funds to a new vendor to close a time-sensitive deal. The voice is convincing, the request is urgent, and the pressure is high.

Hours later, you find out your CEO wasn’t on the call at all. It was an AI-generated clone, and the funds are gone.

Deepfake technology is one of the most potent weapons against brands and their workforces. Every employee, from the C-suite to the front lines, is a target. And these attacks require only minimal effort, as attackers scour the internet for publicly available data to generate lifelike impersonations.

Organizations need deepfake fraud prevention and detection, leveraging AI and advanced monitoring to detect and neutralize synthetic media used for fraudulent purposes.

Let’s explore what deepfake fraud is, how these attacks work, and what your organization needs to do to build a multi-layered defense to protect itself, with Doppel’s help (opens in new tab).

Overview of Deepfake Fraud & Why It Matters

Deepfake fraud is the use of AI-generated voices, images, or videos to impersonate a trusted identity, such as an executive, partner, or influencer, for malicious gain.

Often, this gain is financial, as in the case of a finance employee who was tricked into paying $25 million by a deepfake video meeting.

The rapid spread of this threat is driven by three main factors:

• Accessibility of AI: Open-source deepfake technology is widely available, allowing attackers to easily generate fabricated material.
• Online Exposure: Publicly available video and audio of executives from interviews, earnings calls, and social media posts provide the raw material for AI models to clone an individual’s likeness.
• Brand Visibility: High-profile brands and leaders are prime targets.

And the consequences are severe and extend beyond a single fraudulent transaction, including:

• Direct Financial Loss: Fraudulent wire transfers, unauthorized purchases, and stock manipulation.
• Reputational Damage: Fake videos of executives making inflammatory statements or fake brand ads promoting scams destroy public sentiment.
• Erosion of Internal Trust: When employees can’t trust communications from leadership, internal processes grind to a halt.

Deepfake attacks are a sophisticated form of brand impersonation (opens in new tab) that traditional security tools frequently miss.

What Are Deepfakes? A Quick Technical Primer

The term “deepfake” comes from the technologies used to create one: “deep learning,” a subset of AI, and “fakes.”

Deepfakes are usually created using a model called a generative adversarial network (GAN), primarily taking three forms:

• Image: Creating photorealistic but entirely synthetic photos, or manipulating existing photos.
• Video: Swapping a person’s face onto another’s body in a video or generating a video of them speaking words they’ve never said.
• Audio: Cloning a person’s voice from just a few seconds of sample audio to make it say anything.

The ease of creation with open-source tools has democratized deception at scale. Brands are uniquely vulnerable because their logos, leadership teams, and communications are public-facing, which provides a rich dataset for attackers.

How Deepfake Fraud Works: Common Attack Vectors

Deepfake fraud isn’t one type of attack. It’s actually a tactic used in various social engineering campaigns (opens in new tab). Attackers exploit psychological triggers like urgency, authority, and fear to bypass human judgment.

Common attack vectors include:

• Executive Voice Impersonation: A CEO voice clone calls a finance employee to request an urgent, off-books wire transfer.
• Malicious Video Advertisements: A fake video of a celebrity or brand influencer endorsing a cryptocurrency scam or phishing site is run as a paid ad on social media.
• Synthetic Video Meetings: An attacker joins a Teams or Zoom call, appearing as a real executive, and uses a deepfake video filter to authorize a fraudulent action.
• Manipulated Investor Communications: A fake audio recording of a CFO or CEO is ‘leaked’ to manipulate stock prices.

Attacks like these are often cross-channel, combining a fake email with a follow-up deepfake voice call to build a convincing narrative.

And while some solutions focus on consumer-level voice or biometric fraud, Doppel specializes in detecting the unauthorized use of brand and executive likeness across the digital ecosystem, from social media and domains to app stores and the dark web.

Deepfake Fraud Incident Examples

Key Challenges in Detection & Prevention

Detecting deepfakes is a constant cat-and-mouse game. As detection models improve, so do AI-generation models. It’s why a strategy focused only on pixel-level forensic analysis is incomplete.

Organizations face several key challenges, including:

• Blind Spots: Employees are often untrained to identify deepfakes, particularly as they become increasingly sophisticated.
• Legacy Verification: Traditional verification methods, such as matching a name to a face, fail when the face and voice are synthetic.
• Fragmented Data: Cybersecurity teams usually work in a silo away from the rest of the organization, which means it’s difficult to see a coordinated, cross-channel attack before it’s too late.

Doppel (opens in new tab) mitigates this gap by focusing on continuous cross-platform monitoring and rapid takedown. Instead of just asking if a video is real, we ask, “Is this the brand’s content, and is it in a place it’s not supposed to be?”

A multi-layered defense that combines prevention (policy), detection (technology), and rapid response (action) is the only viable path forward to address the challenges of detecting and preventing a deepfake attack.

How Deepfake Fraud Protection & Detection Works

Deepfake fraud protection is a process of anticipating, identifying, and mitigating synthetic media attacks.

Doppel uses this process for social engineering defense. It’s the intersection of powerful AI-driven algorithms and smart, enforceable governance.

Deepfake Fraud Protection Framework

Prevention Strategies: Before It Happens

Before an attack is ever launched, the best defense is already in place.

Here are the top deepfake prevention strategies:

• Train Staff: Every employee is your most critical checkpoint, so train all staff (opens in new tab) to verify unusual or urgent requests via a secondary channel.
• Strengthen Internal Controls: Roll out multi-factor authentication (MFA) and strict, multi-party verification for all financial transactions.
• Executive Identity Hardening: Audit and control the amount of high-quality voice and video samples of high-profile executives available to the public.
• Proactive Brand Monitoring: Continuously monitoring all digital channels for impersonations, including web domains, social media profiles, and app stores.

Doppel’s brand protection (opens in new tab) and executive impersonation protection (opens in new tab) provide critical support in these areas. The platform automatically scans for unauthorized use of your brand or executive likenesses, flagging anomalies and potential deepfakes before they can be weaponized at scale.

Detection Technologies & Spotting the Synthetic Media

When a deepfake asset surfaces, speed of detection is everything.

The most effective detection techniques use AI to spot telltale signs of synthesis, such as pixel-level inconsistencies, unnatural motion, audio frequency mismatches, or strange blinking patterns.

Any detection signals must then be integrated with your security and fraud platforms, such as SIEM or threat intelligence feeds, to provide a complete picture.

Doppel’s machine learning models are central to this detection layer. By ingesting over 100 million indicators daily, our platform monitors and flags unauthorized or synthetic brand content across web domains, social platforms, and digital marketplaces.

This graph-driven intelligence links signals across platforms to expose the full campaign, and these are the kinds of threat removal solutions that stop attackers (opens in new tab) in their tracks.

Response & Remediation: After an Attack

Once a deepfake is detected, there’s no time to waste. Takedown services (opens in new tab) enable immediate action to prevent deepfake fraud from delivering devastating consequences to brands.

• Incident Response: Your IR should kick in immediately. Confirm the content’s authenticity (or lack thereof), document all evidence, and alert your legal, PR, and leadership teams.
• Rapid Takedown: The primary goal is to limit virality; prioritize takedown of the fake content from its host platform.
• Disclosure: Depending on the severity, you may need to cooperate with law enforcement or disclose the incident to regulatory authorities.

Doppel’s agentic AI automation and expert human analysts manage these takedown workflows (opens in new tab) for you, handling escalations, tracking re-uploads, and reporting on impersonation trends.

Deepfake Fraud Protection: Why It’s Essential & How Doppel Helps

As AI becomes cheaper, more accessible, and more realistic, the risk escalates for any brand with a public-facing presence.

Doing nothing? Not an option. Your brand reputation and financial security can be compromised in moments.

Doppel provides the AI-powered social engineering defense needed for this climate, moving beyond simple detection with:

• Multi-channel coverage that unifies threats across domains, social media, ads, telecommunications, the dark web, and more.
• Graph-driven intelligence to link signals to a real-time threat graph, exposing the entire attacker infrastructure.
• Agentic AI automation that automates correlation, prioritization, and takedowns to disrupt attackers before they go any further.

By analyzing over 100 million indicators daily and achieving media takedown times of 24-48 hours, Doppel provides continuous monitoring and rapid response to combat deepfake fraud.

Book a demo (opens in new tab) today to see Doppel in action, and check out Doppel-pedia (opens in new tab) for a closer look at the insights you need to protect your organization against social engineering attacks.

Frequently Asked Questions

What makes deepfakes different from normal photo or video edits?

The key difference is the use of deep learning (AI models). A simple edit, like using Photoshop to crop an image, is a manual manipulation. A deepfake generates entirely new, hyper-realistic content that can move, speak, and mimic natural human behavior in a way that’s much harder for the human eye or basic software to detect.

This synthetic realism is what makes deepfake impersonation extremely dangerous for executives.

Can multi-factor or biometric authentication stop deepfakes?

While they help, they’re not foolproof. In fact, some of the most advanced deepfake attacks are specifically designed to bypass biometric security systems.

AI-generated voice clones can trick voice-based authentication, and deepfake videos can bypass facial recognition checks. Therefore, multi-factor and biometric authentication must be paired with complementary solutions, such as contextual verification, human validation, and external deepfake monitoring.

Doppel’s solutions operate alongside internal tools, like MFA, to detect synthetic impersonation attempts in the wild.

How can a company measure its exposure to deepfake risk?

Start with a self-assessment: Are you a high-profile brand? Do your executives frequently appear in public-facing videos or audio? Are your brand logos and assets easily accessible?

From there, track metrics like the number of impersonation attempts or false listings detected per quarter. A key part of risk management is ongoing monitoring of online marketplaces and social channels.

Doppel’s platform helps quantify and track these impersonation risks over time, showing where you’re most vulnerable.

When should a brand engage a professional deepfake fraud protection service?

Proactively, rather than waiting for an incident to occur. Once a deepfake video gains traction, the reputational damage is already done.

For any high-profile or high-trust brand (especially in finance, tech, or e-commerce), proactive engagement with a monitoring and takedown platform is necessary.