Adaptive All-In-One AI for Digital ID Safety
Face Fortress
This face verification system uniquely combines deepfake detection with smart quality filtering in a single pipeline. It ensures only high-quality, real faces are verified, delivering trusted results where accuracy is critical.
Key Features & Advantages
Simple Four-Step Pipeline for anti-spoofing: All-in-One Screening
Smart Quality Control: Trusted results
Deepfake Defense
Adaptive Learning: Improves accuracy over time
Efficient & Accurate: Better performance without added complexity
Built for secure biometrics, digital ID, and forensic analysis
Facial verification endeavors to offer dependable biological verification outcomes for applications in domains such as security biometric identification, judicial analysis, and digital identity platforms. Nevertheless, low-quality and malicious deepfake samples resulting from intricate collection environments may give rise to inaccurate results in facial verification. This paper presents a unified system for reliable facial verification, which is designed to combat deepfake-based identity spoofing while guaranteeing the reliability of input quality.
The system processes an input pair of facial images or videos through a multi-module pipeline. Specifically, a facial detection module first crops and aligns the facial regions of the input sample pairs. Subsequently, to measure and exclude low-quality samples, a dual- metric-driven quality assessment module is designed through class-centric deviation and embedding uncertainty fusion. Sequentially, the deepfake detection module filters out the input of illegally synthetic face-swapping samples for anti-spoofing purposes. For genuine samples, a quality-aware verification module trained by self-distillation is proposed for the final facial verification.
During the training process, low-quality samples are guided by high-quality samples to converge towards class centroids by minimizing the Wasserstein distance between their feature distributions, thereby enhancing the model's accuracy without augmenting spatial complexity. Extensive experiments validate the reliability of the proposed system.
