Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning

TL;DR

This paper introduces a comprehensive benchmark and a hierarchical prompt tuning framework for unified face attack detection, capable of handling both physical and digital face forgery threats with improved accuracy and adaptability.

Contribution

It presents UniAttackDataPlus, the largest dataset for face attack detection, and a novel hierarchical prompt tuning method leveraging visual-language models for versatile attack classification.

Findings

The dataset includes 2,875 identities and 54 attack types with nearly 700,000 videos.

The hierarchical prompt tuning framework adaptively selects classification criteria for better detection.

The proposed method outperforms existing models on the new benchmark.

Abstract

PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these two models significantly increases vulnerability towards unknown attacks, burdening deployment environments. The lack of a Unified Face Attack Detection model to simultaneously handle attacks in these two categories is mainly attributed to two factors: (1) A benchmark that is sufficient for models to explore is lacking. Existing UAD datasets only contain limited attack types and samples, leading to the model's confined ability to address abundant advanced threats. In light of these, through an explainable hierarchical way, we propose the most extensive and sophisticated collection of forgery techniques available to date, namely UniAttackDataPlus. Our UniAttackData+ encompasses 2,875 identities and their 54 kinds of corresponding falsified samples, in a total of 697,347 videos. (2) The absence of a trustworthy classification criterion. Current methods endeavor to explore an arbitrary criterion within the same semantic space, which fails to exist when encountering diverse attacks. Thus, we present a novel Visual-Language Model-based Hierarchical Prompt Tuning Framework that adaptively explores multiple classification criteria from different semantic spaces. Specifically, we construct a VP-Tree to explore various classification rules hierarchically. Then, by adaptively pruning the prompts, the model can select the most suitable prompts guiding the encoder to extract discriminative features at different levels in a coarse-to-fine manner. Finally, to help the model understand the classification criteria in visual space, we propose a DPI module to project the visual prompts to the text encoder to help obtain a more accurate semantics.