Deepfake Statistics
Understanding the growing threat of deepfake attacks with real-world data and insights.
Attack Vectors & Impact
Voice Cloning Scams
Adults have experienced an AI voice scam
CEO Fraud Targets
Companies targeted daily
Largest Single Loss
Arup engineering firm (Feb 2024)
Projected Fraud Losses
By 2027 in the U.S. alone
Projected Financial Impact
Deepfake Attack Flow
How sophisticated deepfake attacks are executed in real-world scenarios and how Plurall AI's detection-to-prediction roadmap protects against these threats.
Multi-Modal Video Deepfake Attack Flow
1. Video Deepfake Creation
AI-generated video using GANs/Diffusion models to clone executive likeness
2. Multi-Person Video Call
Sophisticated video conference with multiple deepfaked executives (like Arup $25M case)
3. Coordinated Social Engineering
Video + audio + email coordination to create urgency and bypass verification
4. Financial Loss
Average $500K loss per incident; high-value attacks can reach $25M+
Plurall AI: Detection to Prediction Roadmap
Our comprehensive deepfake detection system analyzes content in real-time, providing actionable insights from detection through prediction. Unlike human detection (24.5% accuracy), our AI-powered system offers enterprise-grade protection.
Content Analysis
Real-time analysis of video frames, audio patterns, and metadata using advanced AI models (GaussMass 1.0, 2.0, 3.0)
Deepfake Detection
Identifies manipulation artifacts, inconsistencies, and AI-generated content with 93% accuracy in under 2 seconds
Threat Assessment
Evaluates risk level, attack sophistication, and potential impact based on detected patterns and historical data
Brand Protection
Comprehensive brand protection through proactive threat detection and automated response, safeguarding your brand integrity before attacks cause both reputation and financial damage
Human Detection Limitation: According to recent research, humans can only correctly identify high-quality deepfake videos 24.5% of the time. Plurall AI's automated detection system provides 93% accuracy, making it essential for protecting against sophisticated attacks.
Attack Frequency
Deepfake attacks occur globally
Annual growth rate in deepfake video volume
Financial Impact
Average loss for large enterprises per incident
Compound annual growth rate for fraud losses
Regional Impact
Deepfake fraud growth in North America (2022-2023)
Losses in Q1 2025 alone in North America
Most Targeted Industries
Social Media
Most targeted sector
Majority of deepfake content is distributed and shared across social media platforms including YouTube, TikTok, Instagram, and X (Twitter), making them prime targets for malicious content distribution.
Legal / Insurance
Second most targeted
AI-driven fraud accounts for 42.5% of all fraud attempts in insurance, with nearly one in three considered successful. Insurance companies saw a 475% increase in synthetic voice fraud attacks in 2024, while law firms face a 1,300% surge in deepfake-enabled fraud.
Source: BenefitsPRO - Insurance AI-Driven Fraud Attacks 2024
Deepfake Type Comparison
Most sophisticated attacks; used for multi-million dollar fraud (e.g., Arup $25M case)
Face swap and virtual camera injection for identity verification bypass
Cheap ($5-10), fast, and highly convincing; 1 in 4 adults have experienced AI voice scams
Case Study
February 2024 - Global Engineering Firm
Single Attack Loss
Video Conference Call
Deepfaked Identities
Attack Details: A finance worker was tricked into wiring $25 millionto accounts controlled by fraudsters. The attack involved a sophisticated, multi-person video conference call featuring deepfaked, AI-generated likenesses of the company's chief financial officer and other senior executives. This case proves that complex, multimodal attacks are no longer theoretical—they are happening now with catastrophic results.