A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection

• URL: http://arxiv.org/abs/2508.20449v1
• Date: Thu, 28 Aug 2025 05:55:28 GMT
• Title: A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection
• Authors: Libo Lv, Tianyi Wang, Mengxiao Huang, Ruixia Liu, Yinglong Wang,
• Abstract summary: We propose a lightweight yet effective architecture for real-time deepfake detection.<n>We design a spatial-frequency hybrid aware module that jointly leverages spatial textures and frequency artifacts.<n>Experiments on several benchmark datasets show that SFMFNet achieves a favorable balance between accuracy and efficiency, with strong generalization and practical value for real-time applications.
• Score: 6.875468802805521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of real-time deepfake generation techniques, forged content is becoming increasingly realistic and widespread across applications like video conferencing and social media. Although state-of-the-art detectors achieve high accuracy on standard benchmarks, their heavy computational cost hinders real-time deployment in practical applications. To address this, we propose the Spatial-Frequency Aware Multi-Scale Fusion Network (SFMFNet), a lightweight yet effective architecture for real-time deepfake detection. We design a spatial-frequency hybrid aware module that jointly leverages spatial textures and frequency artifacts through a gated mechanism, enhancing sensitivity to subtle manipulations. A token-selective cross attention mechanism enables efficient multi-level feature interaction, while a residual-enhanced blur pooling structure helps retain key semantic cues during downsampling. Experiments on several benchmark datasets show that SFMFNet achieves a favorable balance between accuracy and efficiency, with strong generalization and practical value for real-time applications.

Related papers

• FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis [92.23551599659186]
  Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology.<n>FusAD is a unified analysis framework designed for diverse time series tasks.
  arXiv  Detail & Related papers  (2025-12-16T04:34:27Z)
• MAFNet:Multi-frequency Adaptive Fusion Network for Real-time Stereo Matching [2.0202997482095593]
  We propose a Multi-frequency Adaptive Fusion Network (MAFNet) to produce high-quality disparity maps using only efficient 2D convolutions.<n>We show that the proposed MAFNet significantly outperforms existing real-time methods on public datasets such as Scene Flow and KITTI 2015.
  arXiv  Detail & Related papers  (2025-12-04T01:08:38Z)
• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusion [0.4078247440919472]
  FLASH (Frequency-aware LiDAR Adaptive Super-resolution with Hierarchical fusion) is a novel framework that overcomes limitations through dual-domain processing.<n> FLASH integrates two key innovations: (i) Frequency-Aware Window Attention that combines local spatial attention with global frequency-domain analysis via FFT, capturing both fine-grained geometry and periodic scanning patterns at log-linear complexity, and (ii) Adaptive Multi-Scale Fusion that replaces conventional skip connections with learned position-specific feature aggregation, enhanced by CBAM attention for dynamic feature selection.
  arXiv  Detail & Related papers  (2025-11-10T18:38:15Z)
• Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features [44.45692491500845]
  Deepfake and face swap technologies have raised significant concerns in digital security.<n>This study proposes an enhanced deep-learning detection framework that combines handcrafted frequency-domain features with conventional RGB inputs.
  arXiv  Detail & Related papers  (2025-07-28T08:19:22Z)
• CAST: Cross-Attentive Spatio-Temporal feature fusion for Deepfake detection [0.0]
  CNNs are effective at capturing spatial artifacts, and Transformers excel at modeling temporal inconsistencies.<n>We propose a unified CAST model that leverages cross-attention to effectively fuse spatial and temporal features.<n>We evaluate the performance of our model using the FaceForensics++, Celeb-DF, and DeepfakeDetection datasets.
  arXiv  Detail & Related papers  (2025-06-26T18:51:17Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Multiple Contexts and Frequencies Aggregation Network forDeepfake Detection [5.65128683992597]
  Deepfake detection faces increasing challenges since the fast growth of generative models in developing massive and diverse Deepfake technologies. Recent advances rely on introducing features from spatial or frequency domains rather than modeling general forgery features within backbones. We propose an efficient network for face forgery detection named MkfaNet, which consists of two core modules.
  arXiv  Detail & Related papers  (2024-08-03T05:34:53Z)
• 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)
• RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture [24.824596231020585]
  We propose a packet-level network intrusion detection solution that makes use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding. We show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level.
  arXiv  Detail & Related papers  (2023-11-27T17:30:19Z)
• Adaptive Frequency Filters As Efficient Global Token Mixers [100.27957692579892]
  We show that adaptive frequency filters can serve as efficient global token mixers. We take AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet.
  arXiv  Detail & Related papers  (2023-07-26T07:42:28Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

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.