Face2Parts: Exploring Coarse-to-Fine Inter-Regional Facial Dependencies for Generalized Deepfake Detection

• URL: http://arxiv.org/abs/2603.26036v1
• Date: Fri, 27 Mar 2026 03:12:24 GMT
• Title: Face2Parts: Exploring Coarse-to-Fine Inter-Regional Facial Dependencies for Generalized Deepfake Detection
• Abstract summary: We analyze existing forensic methods and observe that each method has unique strengths in detecting deepfake traces.<n>We propose a novel hybrid approach called Face2Parts that takes advantage of coarse-to-fine information to improve deepfake detection.<n>The proposed method achieves an average AUC of 98.42% on FF++, 79.80% on CDF1, 85.34% on CDF2, 89.41% on DFD, 84.07% on DFDC, 95.62% on DTIM, 80.76% on PDD, and 100% on
• Score: 4.511561231517167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimedia data, particularly images and videos, is integral to various applications, including surveillance, visual interaction, biometrics, evidence gathering, and advertising. However, amateur or skilled counterfeiters can simulate them to create deepfakes, often for slanderous motives. To address this challenge, several forensic methods have been developed to ensure the authenticity of the content. The effectiveness of these methods depends on their focus, with challenges arising from the diverse nature of manipulations. In this article, we analyze existing forensic methods and observe that each method has unique strengths in detecting deepfake traces by focusing on specific facial regions, such as the frame, face, lips, eyes, or nose. Considering these insights, we propose a novel hybrid approach called Face2Parts based on hierarchical feature representation ($HFR$) that takes advantage of coarse-to-fine information to improve deepfake detection. The proposed method involves extracting features from the frame, face, and key facial regions (i.e., lips, eyes, and nose) separately to explore the coarse-to-fine relationships. This approach enables us to capture inter-dependencies among facial regions using a channel-attention mechanism and deep triplet learning. We evaluated the proposed method on benchmark deepfake datasets in both intra-, inter-dataset, and inter-manipulation settings. The proposed method achieves an average AUC of 98.42\% on FF++, 79.80\% on CDF1, 85.34\% on CDF2, 89.41\% on DFD, 84.07\% on DFDC, 95.62\% on DTIM, 80.76\% on PDD, and 100\% on WLDR, respectively. The results demonstrate that our approach generalizes effectively and achieves promising performance to outperform the existing methods.

Related papers

• FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes [9.462613446025001]
  Face-fake Deepfake videos pose growing risks to digital security, privacy, and media integrity.<n>FAME is a framework designed to capture subtle artifacts specific to different face-generative models.<n>Results show that FAME consistently outperforms existing methods in both accuracy and runtime.
  arXiv  Detail & Related papers  (2025-06-13T05:47:09Z)
• Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach [77.65459419417533]
  We propose an automated dataset expansion technique to support semantics-oriented DeepFake detection tasks.<n>We also resort to the joint embedding of face images and labels (depicted by text descriptions) for prediction.<n>Our method improves the generalizability of DeepFake detection and renders some degree of model interpretation by providing human-understandable explanations.
  arXiv  Detail & Related papers  (2024-08-29T07:11:50Z)
• 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)
• Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection [33.20064862916194]
  This paper aims to learn potential cross-modal correlation to enhance deepfake detection towards various generation scenarios. Our approach introduces a correlation distillation task, which models the inherent cross-modal correlation based on content information. We present the Cross-Modal Deepfake dataset with four generation methods to evaluate the detection of diverse cross-modal deepfakes.
  arXiv  Detail & Related papers  (2024-04-30T00:25:44Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Towards Generalizable Deepfake Detection by Primary Region Regularization [52.41801719896089]
  This paper enhances the generalization capability from a novel regularization perspective. Our method consists of two stages, namely the static localization for primary region maps, and the dynamic exploitation of primary region masks. We conduct extensive experiments over three widely used deepfake datasets - DFDC, DF-1.0, and Celeb-DF with five backbones.
  arXiv  Detail & Related papers  (2023-07-24T05:43:34Z)
• Learning Diversified Feature Representations for Facial Expression Recognition in the Wild [97.14064057840089]
  We propose a mechanism to diversify the features extracted by CNN layers of state-of-the-art facial expression recognition architectures. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+, and RAF-DB, show the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-10-17T19:25:28Z)
• Detecting Deepfakes with Self-Blended Images [37.374772758057844]
  We present novel synthetic training data called self-blended images ( SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images. We compare our approach with state-of-the-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets.
  arXiv  Detail & Related papers  (2022-04-18T15:44:35Z)
• FakeLocator: Robust Localization of GAN-Based Face Manipulations [19.233930372590226]
  We propose a novel approach, termed FakeLocator, to obtain high localization accuracy, at full resolution, on manipulated facial images. This is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map. Experimental results on popular FaceForensics++, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-01-27T06:15:01Z)

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.