SpoofSense develops AI-driven software for face-liveness detection and deepfake fraud prevention, utilizing passive liveness detection and advanced injection attack detection to identify a wide range of spoofing methods. The technology addresses the growing threat of deepfake injections that can bypass traditional biometric security systems, ensuring robust protection against identity theft and unauthorized access.
Funding
$180K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional biometric security systems are vulnerable to increasingly sophisticated deepfake injection attacks that can bypass standard liveness detection methods. These attacks exploit hardware and software vulnerabilities to introduce digitally altered or synthetic facial imagery, leading to identity theft and unauthorized access.
Solution
SpoofSense offers an AI-driven solution for face-liveness detection and deepfake fraud prevention, combining passive liveness detection with advanced injection attack detection. The technology identifies a wide range of spoofing methods, including virtual cameras, video injections, and faces generated by AI. SpoofSense's algorithms continuously improve detection performance, ensuring robust defense against presentation attacks and deepfake injections. The system processes single image frames from any device, analyzes them for signs of liveness using 3D passive detection methods, and delivers a decision in under 2 seconds.
Target Audience
SpoofSense is designed for businesses requiring robust identity verification and fraud prevention, including those in banking, finance, and other sectors susceptible to biometric fraud.
Features
- Combines standard face presentation attack detection with advanced injection attack detection for multi-layered defense.
- Detects injection-based spoofing using virtual cameras, video injections, and deepfake injections.
- Recognizes and blocks a wide range of attack vectors, including printed images, video replays, cutout masks, 3D silicone masks, recorded videos, and live streams.
- Effectively counters deepfakes, including renderings, face swaps, morphing attacks, and faces created using GenAI algorithms.
- Achieves high accuracy in detecting presentation and deepfake attacks with low false-positive and false-negative rates.
- Offers simple cloud API integration, with on-prem deployment available for data protection.
- Provides 360° spoofing attack coverage, including hyper-realistic masks, video replays, and printed 2D images.
- Trained to perform equally well across all skin tones, genders, and ethnicities, ensuring fairness for every user.