How to Prevent Deepfake Fraud with OSINT

With deepfake technology, anyone can easily produce highly realistic videos, audio, and images, making it nearly impossible to tell what’s real and what’s not. As these tools become increasingly accessible, our ability to trust what we see or hear online is being tested, and skepticism must be practiced at scale, both by individuals and organizations.

While deepfake technology has been creatively used for harmless purposes, such as creating humorous TikTok videos with historical figures or AI avatars of CEOs sharing quarterly results (e.g., Klarna, Zoom), it has also been increasingly exploited for harmful fraudulent activities, including impersonation, social engineering, and investment scams (Source: TFN). Fraudulent use of deepfakes has been seen in financial scams, where fake videos or voice messages impersonate high-level executives or loved ones, leading to significant financial losses.

According to the Financial Industry Regulatory Authority (FINRA), gen AI is increasingly being used by scammers, including to create images featuring fake people holding fabricated ID cards to open new accounts and to set up impostor websites impersonating legitimate companies to lure victims into sending money (Source: WSJ).

Similarly, deepfake technology has also been used to manipulate the public into believing in fraudulent investment opportunities or mislead consumers with fake endorsements from celebrities. In one notorious recent case, a Georgia-based group orchestrated a sophisticated scam that defrauded investors of $35 million by using deepfake videos and fabricated news segments featuring well-known celebrities, including Martin Lewis, Zeo Bell, and Ben Fogle to promote fraudulent crypto schemes.

Also, a few months ago, London Police reported that over £649M was lost due to investment fraud in 2024, with cryptocurrency fraud on the rise as well.

Other types of fraud have been noted such as several cases of impersonators were reported by The Financial Sector Conduct Authority (FSCA), which cautioned consumers against engaging with a Telegram user posing as a representative of Altron TMT and its authorized representative, Werner Gerhard Kapp.

For all these reasons, the UN is urging organizations across various sectors to implement stronger measures to detect and combat AI-driven deepfakes. The call highlights the need for advanced technology to identify manipulated content before it causes widespread harm to individuals and institutions. (Source: Reuters).

At this critical moment, developing more powerful tools and expanding our strategies to detect and prevent deepfake fraud is essential. In this article, we will explore how Open-Source Intelligence (OSINT) can be used effectively in the fight against deepfake fraud.

What is deepfake fraud, and how does it work?

Deepfake fraud refers to the use of deepfake technology to deceive individuals or organizations by creating manipulated content that appears real. Powered by AI and machine learning, this technology can superimpose fake elements onto real media, such as video, audio or images, producing results that are almost impossible to distinguish from genuine content.

One of the most common forms of deepfake fraud involves social engineering. Fraudsters create videos or voice recordings that impersonate someone trusted, such as a family member or business executive, convincing the victim to take actions they normally wouldn’t. For example, a victim may receive a voice note from a loved one requesting an urgent transfer of money after “losing their phone.” The voice sounds identical to the relative’s, but in reality, the message was created using deepfake technology.

Another prevalent form of deepfake fraud involves impersonating high-level executives to carry out financial transactions or steal confidential information. Fraudsters use deepfake videos or audio to impersonate CEOs or senior leaders, convincing employees to approve wire transfers or disclose sensitive data. This type of fraud, sometimes known as CEO fraud, has led to significant financial losses for many organizations and continues to grow in sophistication as deepfake technology becomes more accessible. According to Hong Kong police, a finance employee at a multinational company was duped into transferring $25 million to fraudsters who used deepfake technology to impersonate the company’s chief financial officer during a video conference call.

Lastly, deepfake fraud is also being used in fake investment schemes. Fraudsters may create videos or social media posts that falsely claim celebrities or financial experts are endorsing a particular investment opportunity. These fraudulent schemes prey on innocent individuals and use deepfake technology to provide a false sense of legitimacy. The manipulated content is often convincing enough to lure in victims, making it difficult for them to recognize the fraud until it’s too late.

Common deepfake fraud types

Deepfake technology is increasingly being leveraged in various fraudulent schemes, with fraudsters manipulating digital content to deceive individuals, businesses, and financial institutions and trick them into taking actions they wouldn't normally consider. Here are some of the most common ways deepfake fraud is used:

• Impersonation and social engineering scams
  Fraudsters create convincing deepfake videos or voice recordings that impersonate trusted individuals, such as family members, colleagues, or business executives. Meanwhile, social engineering attacks involve the use of deepfakes to manipulate individuals into revealing sensitive information or making financial decisions.
  • Example: in CEO fraud, deepfake videos of high-level executives are used to authorize fraudulent wire transfers or gain sensitive company information.
  • Example: scammers may use deepfake videos or audio to impersonate someone trusted, manipulating the victim into trusting them enough to provide login credentials, financial details, or other sensitive data.
• Investment scams
  Deepfake videos are used to fabricate endorsements or testimonials from celebrities, influencers, or financial experts, tricking victims into investing in fraudulent schemes. These manipulations create a false sense of legitimacy and confidence in the scam, increasing the likelihood of people parting with their money thanks to the social proof element involved.
• New account opening fraud
  Criminals are using deepfake technology to bypass KYC checks and ID verification processes during new account creation. They can generate synthetic videos or images of either themselves or their ID documents that appear to be legitimate, allowing them to open bank accounts and evade facial recognition or liveness detection measures. This method makes it easier for fraudsters to create fake identities and gain access to financial services, including loans they have no intent to repay or bank accounts that can be used to launder illicit money.
  • Example: a fraudster uses a deepfake video of a real person whose contact details have been leaked in a data breach to apply for a credit card online. The deepfake bypasses the facial recognition system, the automated ID checks validate the photo of the stolen or AI-generated passport, allowing the criminal to open the account and later use it for fraudulent transactions.
• Phishing scams: deepfake tech has elevated phishing scams by allowing fraudsters to create realistic, error-free messages. Previously, phishing emails were easy to spot due to spelling or grammar mistakes, as well as improbable back stories and unprofessional look-and-feel. Now, AI enables criminals to craft contextually relevant, grammatically perfect and visually impeccable emails, making scams harder to detect.
  • Example: a victim receives an email that appears to come from their bank, asking for account verification. The email is well-written and designed, and includes accurate account details, convincing the victim to provide sensitive information, which is then stolen.
• Identity theft and account takeover: deepfakes are used to impersonate individuals, whether a business partner, customer, or even an executive, to gain unauthorized access to existing accounts.
  • Example: fraudsters impersonate a company executive using a deepfake voice or video to convince employees to share credentials or grant access to sensitive company accounts.
  • Example: individuals may fall victim to deepfake identity theft, where fraudsters use fabricated media to impersonate them and gain access to personal or financial accounts.
  • Example: in SIM swap scams, fraudsters impersonate a victim to convince mobile carriers to transfer their phone number to a new SIM card. Once they have control, they can intercept OTPs and bypass 2FA to authorize unauthorized bank transfers. Deepfakes are used to create fake identity documents, like passports, or to impersonate the victim's voice during phone calls, helping criminals bypass phone-based security and access sensitive accounts.

OSINT in preventing deepfake fraud: key methods and applications

Open-source intelligence (OSINT) is an essential tool in the fight against deepfake fraud. It helps businesses, financial institutions, and individuals detect deepfake content by analyzing publicly available information.

OSINT Methods for Deepfake Fraud Prevention

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Detailed breakdown of OSINT methods

1. Reverse image search
   Reverse image searches are one of the most common OSINT techniques used to detect deepfake fraud. Tools like Google Images or TinEye unlock the ability to quickly identify if an image has been used elsewhere on the web, whether it’s a stock photo, a celebrity image, or AI-generated content. For example, a fake investment website may use an image of a well-known celebrity to promote its scam. Running a reverse image search on the celebrity's photo can reveal if it's being used fraudulently across multiple platforms, helping identify the scam. To uncover more about how detailed image analysis can help, explore our article: How to Use Image Analysis to Strengthen Fraud Prevention.
2. Domain reputation analysis
   OSINT tools can analyze the domain associated with a business or website in real time. Using details such as the domain's registration info, history, and IP addresses, it's possible to identify suspicious patterns, such as short-term domain registrations or links to other fraudulent sites. For example, a website offering counterfeit luxury goods may use a newly registered domain with a poor reputation or suspicious registration details, raising a red flag. These indicators suggest that the site is not operating in good faith and may be involved in illegal activities, such as selling counterfeit products or engaging in fraud.
3. Voice and video analysis
   Advanced OSINT tools can analyze voice recordings and videos for signs of manipulation. Deepfake audio often exhibits subtle inconsistencies such as unnatural pauses, odd intonations, or mismatched tones. Similarly, deepfake videos may show irregular facial movements or blinking patterns that are difficult for AI to replicate accurately. OSINT tools can identify whether the media is genuine or fabricated, particularly in cases of CEO fraud or social engineering scams. For instance, it’s possible to reverse-search a video or audio clip to find its source.
4. Metadata scraping
   Scraping metadata from digital files, such as images, videos, and audio recordings, can reveal hidden information like timestamps, editing history, and file origins. For instance, if a video claiming to show a CEO authorizing a transfer has been edited or manipulated, metadata analysis can expose this manipulation. This helps businesses and FIs quickly spot fraudulent deepfake content and avoid making decisions based on false information.
5. Social media cross-referencing
   When checking the legitimacy of a business or individual behind suspicious content, OSINT tools can compare social media profiles across platforms like Facebook, LinkedIn, and Instagram. Consistency is key; fraudulent businesses often have inconsistent or incomplete profiles, which can be detected through cross-referencing. For example, a business profile may have different names or addresses across platforms, suggesting it’s a fake operation designed to deceive consumers or institutions.

Practical applications of OSINT in real-world scenarios

To better understand how these OSINT methods work in practice, let’s look at a few real-world examples:

• Investment scam detection
  Imagine you come across an online investment opportunity endorsed by a well-known celebrity. Using reverse image search, you can check if the celebrity’s imag used comes from a different ad campaign and whether the person involved has shared promotional content about the specific investment or company also on their personal social media accounts. A quick domain reputation analysis might reveal that the website was only recently registered, or that the same company was running ads on Google or Meta for a completely different product until recently. This combination of checks can help you spot a fake investment scheme before you get involved.
• Deepfake voice scam
  You receive a voice message on WhatsApp from what sounds like your daughter, claiming she lost her phone and urgently needs money. The first thing to do is to try and contact your daughter on her usual phone number. If, for whatever reason, you are not able to reach her, you could search for the number that has contacted you on public databases of malicious or disposable phone numbers. You’d be surprised by how often fraudsters reuse contact details for different scams.

Best practices for mitigating deepfake fraud

As deepfake fraud becomes more sophisticated, it’s critical to adopt a proactive approach that combines technology, awareness, and process management. Below are some best practices that can help businesses, financial institutions, and individuals detect and mitigate the risks posed by deepfake fraud:

1. Implement multi-layered verification processes
A single verification method is often not enough to protect against deepfake fraud. To mitigate the risk, businesses should implement a multi-layered verification process that includes several OSINT techniques. For example, using domain analysis, reverse image searches, and voice analysis together provides a more comprehensive check that strengthens the detection of fake businesses or individuals. This approach helps filter out high-risk entities while allowing legitimate ones to pass through seamlessly.

2. Educate employees on deepfake recognition
While technology plays a key role in detecting deepfake fraud, human vigilance is also essential. Businesses should train employees to recognize the signs of deepfake fraud, especially in communication-heavy roles such as customer support, finance, or procurement. Employees should be taught to look for inconsistencies in videos, voice messages, and emails, especially when they are unsolicited or appear unusual. Educating teams about the growing risks of deepfakes can prevent costly errors and ensure that fraudulent activities are caught before they cause damage.

3. Use AI-powered fraud detection tools
AI-powered fraud detection tools are incredibly effective at analyzing large amounts of data quickly. These tools can identify deepfake content with far more accuracy than traditional methods. For example, an AI tool can analyze thousands of videos or images in real-time, comparing them against a database of known deepfake patterns, helping to quickly flag manipulated media. With AI-driven systems implemented into existing fraud prevention processes, businesses can automate many checks, reducing the risk of human error while maintaining high levels of accuracy.

4. Cross-verify info across platforms
Deepfake fraudsters often use inconsistencies across platforms to hide their identity or operations. For example, a business or individual may use different names or addresses on their website, social media, or in email communications. Cross-verifying information across multiple channels is crucial to uncover these discrepancies.

Outmaneuvering deepfakes

The ability to manipulate digital content and create highly realistic videos, audio, or images poses serious risks to businesses, financial institutions, and consumers alike.

Fraudsters will continue to rely on this kind of technology to deceive and manipulate for years to come. However, OSINT solutions, combined with AI and Machine Learning tools, provide the resources businesses and individuals need to detect and mitigate these risks effectively.

So, to learn how you can protect your business against deepfakes and other emerging types of fraud, book a call with our team of fraud-fighting experts.

FAQs

What is deepfake fraud?
Deepfake fraud refers to the use of AI-powered deepfake technology to create fake media, such as videos, images, or audio, that impersonates individuals or organizations. Fraudsters use deepfakes to deceive victims into taking actions, such as transferring money, revealing personal information, or investing in fake opportunities.

How does OSINT help detect deepfake fraud?
OSINT (Open-Source Intelligence) helps detect deepfake fraud by analyzing publicly available data, such as social media profiles, domain registrations, and media files. OSINT tools can verify the authenticity of digital content and identify signs of manipulation, such as mismatched metadata, suspicious domains, or altered videos.

What are the most common uses of deepfake fraud?
Deepfake fraud is commonly used in social engineering attacks, where fraudsters impersonate trusted individuals (e.g., family members, executives) to trick victims. It's also used in investment scams, where fake endorsements from celebrities or financial experts promote fraudulent schemes, and CEO fraud, where deepfakes are used to impersonate business leaders and authorize fraudulent transactions.

Can OSINT tools detect AI-generated images?
Yes, OSINT tools can detect AI-generated images using reverse image search and specialized AI detection tools that identify digital artifacts and inconsistencies that are common in fake images. These tools help identify manipulated media before it can be used for fraud.

How can businesses integrate OSINT into their fraud prevention strategies?
Businesses can integrate OSINT into their fraud prevention strategies by implementing automated OSINT tools during onboarding processes, verifying domain reputations, cross-referencing social media profiles, and using metadata analysis to detect deepfakes. This ensures that only legitimate businesses and individuals are onboarded, while fraudulent entities are flagged and prevented from accessing sensitive data or services.

What should I do if I suspect a deepfake scam?
If you suspect a deepfake scam, conduct thorough verification using OSINT tools like reverse image searches, domain analysis, social media lookup by email or voice matching, depending on the type of content. You should also report the incident to the relevant authorities or your fraud prevention team to take appropriate action.