A document template attack involves fraudsters creating or manipulating templates of official documents, such as IDs, passports, or driver’s licenses, to mimic legitimate documents which they can then use to pass identity verification checks.
Generative AI has enabled fraudsters to take document forgeries to the next level, making them look authentic including precise matching of fonts, layouts, holograms, seals, and watermarks. Moreover, these algorithms can “self-learn” and refine their outputs, making today’s fake documents virtually indistinguishable from the real thing.
The U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) issued a fraud schemes alert following analysis of Bank Secrecy Act (BSA) data indicating that financial institutions often detect GenAI and synthetic content in identity documents by conducting re-reviews of a customer’s account opening documents.
A delay in detecting identifying fraud such as fraud initiated by document template attack can lead to identity theft, unauthorized account access, and financial losses for businesses and customers alike.
How do document template attacks work?
Fraudsters use template attacks to exploit limitations in automated verification systems that rely solely on capabilities such as pattern recognition, template matching, and edge detection. These systems may have difficulty detecting subtle anomalies or mismatched details that indicate fraud.
Bad actors often combine document template attacks with other forms of fraud to increase their chances of success. For instance, they may use social engineering tactics to steal personal data. By combining real data with fabricated elements in document templates, attackers create a synthetic identity that looks even more realistic to verification systems.
By exploiting these vulnerabilities, fraudsters are able to bypass verification systems and carry out large-scale fraud operations with a higher level of success.
The evolution of document forgeries
The methods behind identity document forgery have evolved dramatically, fueled by digital tools and, more recently, Generative AI.
Initially, forgeries involved manual techniques, such as altering details on physical documents. As automated identity verification systems were implemented, fraudsters turned to more sophisticated tools like desktop publishing software to mimic official documents.
Even more concerning, legitimate templates and high-quality AI-driven replicas are now readily available online. Generative AI tools have become widely available, allowing even low-skill fraudsters to create convincing forgeries.
Gerald van Veldhuijsen from ABN AMRO talks about fighting AI fraud:
The impact of Generative AI on document template attacks
AI document forgeries are designed to exploit the limitations of identity verification systems, many of which rely on template matching, pattern recognition, or low-level feature analysis. Since AI-generated documents closely mimic these characteristics, they often pass automated systems undetected.
Generative AI has made template attacks significantly easier for fraudsters in several specific ways:
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Realistic document creation: AI models like GANs (Generative Adversarial Networks) can be trained on vast datasets of legitimate documents to generate highly realistic document forgeries. These models can learn to replicate even subtle security elements, such as microtext, watermarks, or fine-grain textures, making it incredibly challenging for traditional template-matching systems to detect fraud.
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Quality document creation at scale: Generative AI allows fraudsters to create high-quality fake documents at scale. Templates can be altered in bulk with minimal effort or skill to create countless variations that slip past automated verification systems. When organized crime rings have a forgery that successfully passes an identity check, they can replicate the template and use it repeatedly to quickly open accounts in many names and often, across multiple institutions.
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Synthetic identity generation: Document templates may be used with synthetic identities that combine real data, like addresses or social security numbers, with fake elements. This enables fraudsters to create entirely new identities on realistic templates that look legitimate to databases. The tactic enables fraudsters to execute schemes at a large scale without reusing the same identity or duplicate data. Synthetic identities built on genuine-looking templates may pass initial verification, allowing fraudsters to open accounts, secure loans, or gain unauthorized access before the deception is uncovered.
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Deepfake identity images: Generative AI can create fake or synthetic images of people which can be used on a document or generated to closely resemble a photo on a document. Deepfake content can also be injected into a video-based or photo-based verification process to thwart face matching and liveness detection.
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Rapid adaptation: AI allows fraudsters to iterate quickly when a new security feature is introduced, such as a redesigned driver's license. AI can be retrained on updated templates, enabling fraudsters to adapt at a much faster rate than ever before. AI algorithms can also learn from both failed and successful attempts, enabling fraudsters to adjust their tactics and target vulnerabilities.
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Methods to combat document template attacks
Combating high-quality template attacks requires a layered defense.
Foundational tools, like AAMVA checks in the United States and verification of data stored on NFC chips in official IDs, play an important role in detecting fraud.
Combining document verification with biometric matching (LINK, is this a key word somewhere, if not do liveness?) and liveness detection is now standard for detecting common fraud tactics, including impersonation and spoofing. This approach strengthens security by verifying both the document and the person presenting it.
While these capabilities are excellent at catching fraud, they are not immune to advanced tactics designed to circumvent safeguards.
Today’s identity verification solutions must take a comprehensive approach to fighting fraud. Countering AI-generated forgeries requires adaptive, AI-based detection models. When it comes to template attacks, modern capabilities include:
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Velocity scans for duplicate identities: The ability to flag the recurrence of duplicate selfie or document portraits.
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Ring attack detection: The ability to detect repeated document similarities or recurring background around the document.
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Known fraud gallery checks: the ability to add fraudulent content to a database of known templates, to prevent repeated attacks from the same templates.
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Digital manipulation and deepfake detection: the ability to detect digitally generated or manipulated content that may be use in the creation of a fraudulent document or injected into the identity verification process to pass integrated biometric matching. Solutions should be capable of examining digital artifacts across multiple layers within the media to not only detect popular generators, but also more complex or less common tools.
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Injection attack detection: Prevent AI-generated images or videos from being injected into identity verification systems via virtual cameras or emulators. Solutions should analyze the image's content and monitor the transmission channel from which the media originates, ensuring a more robust defense.
Staying ahead of document template attacks
AI fraud moves fast. A modern identity verification solution must be designed to not only detect current fraud types but to future-proof against emerging fraud techniques. Adaptive algorithms like those used by Mitek’s Digital Fraud Defender continuously learn to recognize new attack patterns and help mitigate the impact of AI-powered forgeries.
There is no single solution to complex fraud—especially as fraudsters combine different tactics to avoid detection. A multi-layered identity verification strategy, utilizing adaptive AI-driven defenses, is essential to stay ahead.
Conclusion
The evolution of document forgery, from manual alterations to advanced generative AI-powered fakes, underscores the ongoing battle between fraudsters and verification providers.
Document template attacks now leverage AI and advanced forgery techniques in ways that are difficult to detect by conventional identity verification systems. To combat these attacks, businesses need robust, multi-layered defenses that go beyond template matching, incorporating AI, biometric checks, and data signals to create a holistic security approach.
Learn more about Mitek's Digital Fraud Defender
About Kim Martin - VP Global Growth Marketing at Mitek
Kim Martin is VP Global Growth Marketing at Mitek.