Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection

TL;DR

This paper introduces a unified contrastive learning framework for detecting both physical and digital face spoofing attacks, improving accuracy and efficiency over separate models.

Contribution

It presents a novel paired-sampling contrastive approach that learns modality-agnostic liveness cues for joint face attack detection, reducing complexity and inference time.

Findings

Achieves 2.10% ACER on benchmark

Lightweight model with 4.46 GFLOPs

Trains in under one hour

Abstract

Modern face recognition systems remain vulnerable to spoofing attempts, including both physical presentation attacks and digital forgeries. Traditionally, these two attack vectors have been handled by separate models, each targeting its own artifacts and modalities. However, maintaining distinct detectors increases system complexity and inference latency and leaves systems exposed to combined attack vectors. We propose the Paired-Sampling Contrastive Framework, a unified training approach that leverages automatically matched pairs of genuine and attack selfies to learn modality-agnostic liveness cues. Evaluated on the 6th Face Anti-Spoofing Challenge Unified Physical-Digital Attack Detection benchmark, our method achieves an average classification error rate (ACER) of 2.10 percent, outperforming prior solutions. The framework is lightweight (4.46 GFLOPs) and trains in under one hour, making it practical for real-world deployment. Code and pretrained models are available at https://github.com/xPONYx/iccv2025_deepfake_challenge.