Pith. sign in

REVIEW 5 cited by

SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.04178 v2 pith:CWKCWA2R submitted 2024-02-06 cs.CV

Yichen Shi , Yuhao Gao , Yingxin Lai , Hongyang Wang , Jun Feng , Lei He , Jun Wan , Changsheng Chen
show 2 more authors
Zitong Yu Xiaochun Cao
This is my paper · ORCID
classification cs.CV
keywords facedetectionforgeryevaluatemllmsmultimodalspoofingtasks
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabilities. However, their ability to detect subtle visual spoofing and forgery clues in face attack detection tasks remains underexplored. In this paper, we introduce a benchmark, SHIELD, to evaluate MLLMs for face spoofing and forgery detection. Specifically, we design true/false and multiple-choice questions to assess MLLM performance on multimodal face data across two tasks. For the face anti-spoofing task, we evaluate three modalities (i.e., RGB, infrared, and depth) under six attack types. For the face forgery detection task, we evaluate GAN-based and diffusion-based data, incorporating visual and acoustic modalities. We conduct zero-shot and few-shot evaluations in standard and chain of thought (COT) settings. Additionally, we propose a novel multi-attribute chain of thought (MA-COT) paradigm for describing and judging various task-specific and task-irrelevant attributes of face images. The findings of this study demonstrate that MLLMs exhibit strong potential for addressing the challenges associated with the security of facial recognition technology applications.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing

    cs.CV 2026-07 conditional novelty 5.0 of 10

    FAS-R1 combines long-CoT supervised fine-tuning with difficulty-aware GRPO and degradation-simulated augmentation to improve multi-task face anti-spoofing and explainable rationales.

  2. ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    ForenX detects AI-generated images with MLLMs guided by a forensic prompt and trained on a new explanation dataset, ForgReason.

  3. FaceLLM: A Multimodal Large Language Model for Face Understanding

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Fine-tuning InternVL3 on ChatGPT-generated face QA pairs yields a face-specialized MLLM with the highest reported accuracy among MLLMs on FaceXBench.

  4. Visual Language Models as Zero-Shot Deepfake Detectors

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Zero-shot VLMs scored by normalized yes/no token probabilities beat most trained deepfake detectors on a new SimSwap dataset, and a lightly fine-tuned InstructBLIP is near-perfect on DFDC-P.

  5. Survey on AI-Generated Media Detection: From Non-MLLM to MLLM

    cs.CV 2025-02 unverdicted novelty 3.0 of 10

    A survey organizing AI-generated media detection into Non-MLLM and MLLM based methods, with task and benchmark taxonomies.

Pith tools