Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector

• URL: http://arxiv.org/abs/2503.20188v1
• Date: Wed, 26 Mar 2025 03:28:46 GMT
• Title: Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
• Authors: Xiao Guo, Xiufeng Song, Yue Zhang, Xiaohong Liu, Xiaoming Liu,
• Abstract summary: Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation.<n>We introduce a novel method capable of generating both simultaneously.<n>Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models.
• Score: 21.19305310480085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separately, we introduce a novel method capable of generating both simultaneously. Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models (LLMs) to enhance both the generalization and explainability of deepfake detection. Specifically, we introduce a multi-modal face forgery detector (M2F2-Det) that employs tailored face forgery prompt learning, incorporating the pre-trained CLIP to improve generalization to unseen forgeries. Also, M2F2-Det incorporates an LLM to provide detailed textual explanations of its detection decisions, enhancing interpretability by bridging the gap between natural language and subtle cues of facial forgeries. Empirically, we evaluate M2F2-Det on both detection and explanation generation tasks, where it achieves state-of-the-art performance, demonstrating its effectiveness in identifying and explaining diverse forgeries.

Related papers

• Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks [50.53590930588431]
  adversarial examples pose serious threats to natural language processing systems. Recent studies suggest that adversarial texts deviate from the underlying manifold of normal texts, whereas masked language models can approximate the manifold of normal data. We first introduce Masked Language Model-based Detection (MLMD), leveraging mask unmask operations of the masked language modeling (MLM) objective.
  arXiv  Detail & Related papers  (2025-04-08T14:10:57Z)
• VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models [14.053424085561296]
  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.
  arXiv  Detail & Related papers  (2025-03-08T09:55:19Z)
• 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)
• LLM-based Translation Inference with Iterative Bilingual Understanding [52.46978502902928]
  We propose a novel Iterative Bilingual Understanding Translation method based on the cross-lingual capabilities of large language models (LLMs)<n>The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately.<n>The proposed IBUT outperforms several strong comparison methods.
  arXiv  Detail & Related papers  (2024-10-16T13:21:46Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models [51.21351775178525]
  DiffExplainer is a novel framework that, leveraging language-vision models, enables multimodal global explainability. It employs diffusion models conditioned on optimized text prompts, synthesizing images that maximize class outputs. The analysis of generated visual descriptions allows for automatic identification of biases and spurious features.
  arXiv  Detail & Related papers  (2024-04-03T10:11:22Z)
• SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection [18.356648843815627]
  Out-of-context (OOC) misinformation is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments. We introduce SNIFFER, a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation.
  arXiv  Detail & Related papers  (2024-03-05T18:04:59Z)
• SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models [63.946809247201905]
  We introduce a new benchmark, namely SHIELD, to evaluate the ability of MLLMs on face spoofing and forgery detection. We design true/false and multiple-choice questions to evaluate multimodal face data in these two face security tasks. The results indicate that MLLMs hold substantial potential in the face security domain.
  arXiv  Detail & Related papers  (2024-02-06T17:31:36Z)
• Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection [72.36017150922504]
  We propose a multi-modal contextual knowledge distillation framework, MMC-Det, to transfer the learned contextual knowledge from a teacher fusion transformer to a student detector. The diverse multi-modal masked language modeling is realized by an object divergence constraint upon traditional multi-modal masked language modeling (MLM)
  arXiv  Detail & Related papers  (2023-08-30T08:33:13Z)
• Beyond Bounding Box: Multimodal Knowledge Learning for Object Detection [3.785123406103386]
  We take advantage of language prompt to introduce effective and unbiased linguistic supervision into object detection. We propose a new mechanism called multimodal knowledge learning (textbfMKL), which is required to learn knowledge from language supervision.
  arXiv  Detail & Related papers  (2022-05-09T07:03:30Z)

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.