Anatomía de un ataque de identidad

The Identity Crisis Explained

Artificial intelligence holds enormous potential — but it’s also being weaponized to create a global identity crisis. Sophisticated synthetic media is eroding trust in governments, businesses, and people.

The biometric threat landscape has fundamentally changed. What once required high technical skill is now achievable through easy-to-use tools, marketplaces, and guides. This democratization of attack capability has created an explosive increase in scale and reach. Bad actors are also taking advantage of innovative technology, however, and are increasingly using generative AI to develop evermore sophisticated ways to commit fraud, launder money, or commit other illicit activities for financial gain. Unfortunately, while most biometric solutions have developed resilience to presentation attacks, many are struggling to defend against the much easier-to-create and more scalable digital injection attacks like deepfakes, face swaps, and even synthetic identities.

To help organizations better understand the anatomy of an identity attack, iProov is sharing insights gained from our Security Operations Center (iSOC). iSOC utilizes state-of-the-art machine-learning computer vision systems in conjunction with complementary, multimodal approaches to detect biometric attack patterns across multiple geographies, devices, and platforms.

iSOC Sees Criminals Take Digital Injection Attacks to Another Level:

• Threat actors are advancing digital injection attacks across platforms, targeting mobile web, native Android, and native iOS via emulators.
• The emergence and growth of sophisticated face swaps and synthetic identities suggest that low-skilled criminals now have the means to launch advanced attacks.
• Threat actors are launching motion-based attacks simultaneously and at scale against hundreds of systems globally.

Understanding and uncovering the anatomy of an identity attack is essential when selecting which biometric solutions are best suited for your organization’s needs.

Siga leyendo para obtener más información sobre el panorama de las amenazas biométricas, descubrir las tendencias de comportamiento de los actores de las amenazas y comprender por qué no todas las tecnologías biométricas son iguales a la hora de enfrentarse a estas amenazas.

Understanding Biometric Attack Types

Presentación Ataques

Attackers use photos, masks, or synthetic media in front of a camera. PAD-accredited solutions catch many, but AI imagery is making these spoofs far more convincing.

Ataques de inyección digital

A much bigger threat: injecting synthetic media directly into the data stream. These attacks:

• Scale infinitely
• Require no physical presence
• Outnumbered presentation attacks 10:1 in late 2022

Common Attack Techniques

Synthetic Identity Fraud (SIF)

• Mixes real + fake data
• GANs generate realistic faces
• Builds trust over time, making detection harder
• Projected to cost $23B by 2030

Face Swaps

• Overlay real biometrics onto attacker images
• Enable account takeovers
• Rose 300% in 2024

Image-to-Video Conversion

• Turns still photos into realistic video
• Simplifies synthetic identity creation
• Bypasses basic liveness checks

Native Virtual Camera Attacks

• Inject AI or pre-recorded footage directly
• 2665% increase in 2024
• Mainstream apps made these tools widely available

Crime-as-a-Service

• Dark web kits + tutorials for non-experts
• 31+ new threat groups identified in 2024

The Human Detection Failure

• Only 0.1% of people spotted all synthetic examples
• Trained pros reached just 50% accuracy
• Human operator verification is being bypassed
• Overconfidence fuels costly lapses

Can you spot a deepfake? Take the deepfake detection test!

The Financial Impact

• $8.8B in identity losses (2023)
• $10.2B in fraud losses (H1 2024)
• $4.24M average per security incident
• $25.6M lost in a single deepfake-powered scam (Hong Kong)

The iProov Solution: Science-Based Face Verification

Not All Biometrics Are Equal

Basic liveness fails against AI. iProov’s AI-powered face verification ensures genuine presence.

The Multi-Layered Defense

1. The Right Person – Match to official ID
2. A Real Person – Detect synthetic media
3. Real-Time Presence – Stop replays with one-time challenges
4. Managed Detection & Response – Constant monitoring + updates

The Cloud Advantage

• Harder to reverse-engineer
• Instant updates against new threats
• Shared intelligence across platforms
• Trusted ID binding

Continuous Threat Intelligence

The Low Attack Rate Paradox

Strong deterrence keeps attack volumes low — fraudsters move on.

Real-Time Adaptation

• Constant threat monitoring
• Rapid zero-day response
• Automation + expert analysis
• Proactive threat hunting

Beyond Traditional Standards

Certifications (NIST, iBeta) are baseline — not enough. Look for:

• Independent proof of detection strength
• Live adaptation to new threats
• Active threat intel programs
• Collaboration with defense leaders like MITRE

The Future of Identity Security

94% of organizations agree: vendors must deliver more than software. They must provide a living, evolving defense service:

• Real-time threat monitoring
• Continuous detection updates
• Multi-modal biometrics
• Proactive, intelligence-led defense