DiffusionFF: Face Forgery Detection via Diffusion-based Artifact Localization

• URL: http://arxiv.org/abs/2508.01873v1
• Date: Sun, 03 Aug 2025 18:06:04 GMT
• Title: DiffusionFF: Face Forgery Detection via Diffusion-based Artifact Localization
• Authors: Siran Peng, Haoyuan Zhang, Li Gao, Tianshuo Zhang, Bao Li, Zhen Lei,
• Abstract summary: DiffusionFF is a novel framework that enhances face forgery detection through diffusion-based artifact localization.<n>Our method utilizes a denoising diffusion model to generate high-quality Structural Dissimilarity (DSSIM) maps, which effectively capture subtle traces of manipulation.
• Score: 21.139016641596676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid evolution of deepfake generation techniques demands robust and accurate face forgery detection algorithms. While determining whether an image has been manipulated remains essential, the ability to precisely localize forgery artifacts has become increasingly important for improving model explainability and fostering user trust. To address this challenge, we propose DiffusionFF, a novel framework that enhances face forgery detection through diffusion-based artifact localization. Our method utilizes a denoising diffusion model to generate high-quality Structural Dissimilarity (DSSIM) maps, which effectively capture subtle traces of manipulation. These DSSIM maps are then fused with high-level semantic features extracted by a pretrained forgery detector, leading to significant improvements in detection accuracy. Extensive experiments on both cross-dataset and intra-dataset benchmarks demonstrate that DiffusionFF not only achieves superior detection performance but also offers precise and fine-grained artifact localization, highlighting its overall effectiveness.

Related papers

• Anomaly detection using Diffusion-based methods [15.049468347670421]
  This paper explores the utility of diffusion-based models for anomaly detection.<n>It focuses on their efficacy in identifying deviations in both compact and high-resolution datasets.
  arXiv  Detail & Related papers  (2024-12-10T14:17:23Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion [94.46904504076124]
  Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. We introduce DiffusionFake, a novel framework that reverses the generative process of face forgeries to enhance the generalization of detection models.
  arXiv  Detail & Related papers  (2024-10-06T06:22:43Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• 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)
• DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention [12.36906630199689]
  We construct a DA-HFNet forged image dataset guided by text or image-assisted GAN and Diffusion model. Our goal is to utilize a hierarchical progressive network to capture forged artifacts at different scales for detection and localization.
  arXiv  Detail & Related papers  (2024-06-03T16:13:33Z)
• 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)
• Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection [96.539862328788]
  Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. We propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF) Experiment results on several public face forgery datasets demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T08:58:49Z)
• Exposing the Fake: Effective Diffusion-Generated Images Detection [14.646957596560076]
  This paper proposes a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID) SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising errors. Our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.
  arXiv  Detail & Related papers  (2023-07-12T16:16:37Z)

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.