VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models

• URL: http://arxiv.org/abs/2503.06142v1
• Date: Sat, 08 Mar 2025 09:55:19 GMT
• Title: VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models
• Authors: Xinan He, Yue Zhou, Bing Fan, Bin Li, Guopu Zhu, Feng Ding,
• Abstract summary: Face models with high-quality and controllable attributes pose a significant challenge for Deepfake detection.<n>In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics.<n>We propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators.
• Score: 14.053424085561296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only yield a binary decision, incapable of forgery localization, attribution of forgery methods, and providing analysis on the cause of forgeries. In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics, and propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators. To achieve the above goals, we introduce VLF (Visual Language Forensics), a novel and diverse synthesis face dataset designed to facilitate rich interactions between Visual and Language modalities in MLLMs. Additionally, we propose an extrinsic knowledge-guided description method, termed EkCot, which leverages knowledge from the image generation pipeline to enable MLLMs to quickly capture image content. Furthermore, we introduce a low-level vision comparison pipeline designed to identify differential features between real and fake that MLLMs can inherently understand. These features are then incorporated into EkCot, enhancing its ability to analyze forgeries in a structured manner, following the sequence of detection, localization, and attribution. Extensive experiments demonstrate that VLForgery outperforms other state-of-the-art forensic approaches in detection accuracy, with additional potential for falsified region localization and attribution analysis.

Related papers

• Unlocking the Capabilities of Vision-Language Models for Generalizable and Explainable Deepfake Detection [18.125287697902813]
  Current vision-language models (VLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains underexplored for deepfake detection. We present a novel paradigm that unlocksVLMs' potential capabilities through three components.
  arXiv  Detail & Related papers  (2025-03-19T03:20:03Z)
• Towards General Visual-Linguistic Face Forgery Detection(V2) [90.6600794602029]
  Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust.<n>Recent works demonstrate that leveraging multimodal models can enhance the generalization and interpretability of face forgery detection.<n>We propose Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification.
  arXiv  Detail & Related papers  (2025-02-28T04:15:36Z)
• Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis [0.1984949535188529]
  Vision language models (VLMs) address this by incorporating a pretrained vision backbone for processing images and a cross-modal projector.<n>We developed intelligent assistants finetuned from LLaVA models to enhance multimodal understanding in low-dose radiation therapy.
  arXiv  Detail & Related papers  (2025-01-26T02:48:01Z)
• ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection [107.86009509291581]
  We propose ForgerySleuth to perform comprehensive clue fusion and generate segmentation outputs indicating regions that are tampered with.<n>Our experiments demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in robustness, generalization, and explainability.
  arXiv  Detail & Related papers  (2024-11-29T04:35:18Z)
• ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [49.12958154544838]
  ForgeryGPT is a novel framework that advances the Image Forgery Detection and localization task.<n>It captures high-order correlations of forged images from diverse linguistic feature spaces.<n>It enables explainable generation and interactive dialogue through a newly customized Large Language Model (LLM) architecture.
  arXiv  Detail & Related papers  (2024-10-14T07:56:51Z)
• VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection [19.79027968793026]
  Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects. Existing ZSAD methods are limited by closed-world settings, struggling to unseen defects with predefined prompts. We propose a novel framework VMAD (Visual-enhanced MLLM Anomaly Detection) that enhances MLLM with visual-based IAD knowledge and fine-grained perception.
  arXiv  Detail & Related papers  (2024-09-30T09:51:29Z)
• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored. We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models [61.8876114116716]
  Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks. However, their ability to detect subtle visual spoofing and forgery clues in face attack detection tasks remains underexplored. We introduce a benchmark, SHIELD, to evaluate MLLMs for face spoofing and forgery detection.
  arXiv  Detail & Related papers  (2024-02-06T17:31:36Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.