D2Fusion: Dual-domain Fusion with Feature Superposition for Deepfake Detection

• URL: http://arxiv.org/abs/2503.17184v1
• Date: Fri, 21 Mar 2025 14:31:33 GMT
• Title: D2Fusion: Dual-domain Fusion with Feature Superposition for Deepfake Detection
• Authors: Xueqi Qiu, Xingyu Miao, Fan Wan, Haoran Duan, Tejal Shah, Varun Ojhab, Yang Longa, Rajiv Ranjan,
• Abstract summary: Current Deepfake detection methods fail to thoroughly explore artifact information across different domains.<n>We introduce a novel bi-directional attention module to capture the local positional information of artifact clues from the spatial domain.<n>By doing so, we can obtain high-frequency information in the fine-grained features, which contains the global and subtle forgery information.
• Score: 5.281969205292727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfake detection is crucial for curbing the harm it causes to society. However, current Deepfake detection methods fail to thoroughly explore artifact information across different domains due to insufficient intrinsic interactions. These interactions refer to the fusion and coordination after feature extraction processes across different domains, which are crucial for recognizing complex forgery clues. Focusing on more generalized Deepfake detection, in this work, we introduce a novel bi-directional attention module to capture the local positional information of artifact clues from the spatial domain. This enables accurate artifact localization, thus addressing the coarse processing with artifact features. To further address the limitation that the proposed bi-directional attention module may not well capture global subtle forgery information in the artifact feature (e.g., textures or edges), we employ a fine-grained frequency attention module in the frequency domain. By doing so, we can obtain high-frequency information in the fine-grained features, which contains the global and subtle forgery information. Although these features from the diverse domains can be effectively and independently improved, fusing them directly does not effectively improve the detection performance. Therefore, we propose a feature superposition strategy that complements information from spatial and frequency domains. This strategy turns the feature components into the form of wave-like tokens, which are updated based on their phase, such that the distinctions between authentic and artifact features can be amplified. Our method demonstrates significant improvements over state-of-the-art (SOTA) methods on five public Deepfake datasets in capturing abnormalities across different manipulated operations and real-life.

Related papers

• Generalizable Deepfake Detection via Effective Local-Global Feature Extraction [5.221473306027505]
  GANs and diffusion models have led to the generation of increasingly realistic fake images.<n>Deepfake detection has become a pressing issue in today's world.<n>We propose a novel method that effectively combines local and global features.
  arXiv  Detail & Related papers  (2025-01-25T15:53:57Z)
• Frequency-Spatial Entanglement Learning for Camouflaged Object Detection [34.426297468968485]
  Existing methods attempt to reduce the impact of pixel similarity by maximizing the distinguishing ability of spatial features with complicated design. We propose a new approach to address this issue by jointly exploring the representation in the frequency and spatial domains, introducing the Frequency-Spatial Entanglement Learning (FSEL) method. Our experiments demonstrate the superiority of our FSEL over 21 state-of-the-art methods, through comprehensive quantitative and qualitative comparisons in three widely-used datasets.
  arXiv  Detail & Related papers  (2024-09-03T07:58:47Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection [57.646582245834324]
  We propose a simple yet effective deepfake detector called LSDA. It is based on a idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary. We show that our proposed method is surprisingly effective and transcends state-of-the-art detectors across several widely used benchmarks.
  arXiv  Detail & Related papers  (2023-11-19T09:41:10Z)
• 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)
• Cross-Domain Local Characteristic Enhanced Deepfake Video Detection [18.430287055542315]
  Deepfake detection has attracted increasing attention due to security concerns. Many detectors cannot achieve accurate results when detecting unseen manipulations. We propose a novel pipeline, Cross-Domain Local Forensics, for more general deepfake video detection.
  arXiv  Detail & Related papers  (2022-11-07T07:44:09Z)
• MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection [80.83725644958633]
  Current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. We present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation.
  arXiv  Detail & Related papers  (2021-09-15T14:11:53Z)
• Video Salient Object Detection via Adaptive Local-Global Refinement [7.723369608197167]
  Video salient object detection (VSOD) is an important task in many vision applications. We propose an adaptive local-global refinement framework for VSOD. We show that our weighting methodology can further exploit the feature correlations, thus driving the network to learn more discriminative feature representation.
  arXiv  Detail & Related papers  (2021-04-29T14:14:11Z)
• Multi-attentional Deepfake Detection [79.80308897734491]
  Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. We propose a new multi-attentional deepfake detection network. Specifically, it consists of three key components: 1) multiple spatial attention heads to make the network attend to different local parts; 2) textural feature enhancement block to zoom in the subtle artifacts in shallow features; 3) aggregate the low-level textural feature and high-level semantic features guided by the attention maps.
  arXiv  Detail & Related papers  (2021-03-03T13:56:14Z)
• Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation [62.29076080124199]
  This paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains.
  arXiv  Detail & Related papers  (2020-03-23T13:40:06Z)

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.