DYMAPIA: A Multi-Domain Framework for Detecting AI-based Video Manipulation

• URL: http://arxiv.org/abs/2604.24426v1
• Date: Mon, 27 Apr 2026 12:53:43 GMT
• Title: DYMAPIA: A Multi-Domain Framework for Detecting AI-based Video Manipulation
• Abstract summary: We introduce DYMAPIA, a multi-domain Deepfake detection framework.<n>It fuses spatial, spectral, and temporal cues to capture subtle traces of manipulation in visual data.<n>System builds dynamic anomaly masks by combining evidence from Fourier spectra, local texture descriptors, edge irregularities, and optical flow consistency.
• Score: 2.8647133890966994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-generated media are advancing rapidly, raising pressing concerns for content authenticity and digital trust. We introduce DYMAPIA, a multi-domain Deepfake detection framework that fuses spatial, spectral, and temporal cues to capture subtle traces of manipulation in visual data. The system builds dynamic anomaly masks by combining evidence from Fourier spectra, local texture descriptors, edge irregularities, and optical flow consistency, which highlight tampered regions with fine spatial accuracy. These masks guide DistXCNet, a lightweight classifier distilled from Xception and optimized with depthwise separable convolutions for fast, region-focused classification. This joint design achieves state-of-the-art results, with accuracy and F1-scores exceeding 99\% on FF++, Celeb-DF, and VDFD benchmarks, while keeping the model compact enough for real-time use. Beyond outperforming existing full-frame and multidomain detectors, DYMAPIA demonstrates deployment readiness for time-critical forensic tasks, including media verification, misinformation defense, and secure content filtering.

Related papers

• SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection [0.0]
  The rapid proliferation of AI-generated images (AIGI) presents a significant challenge to digital information integrity.<n>This paper introduces SPECTRA-Net, a scalable pipeline for explainable, cross-domain tensor representations for AIGI detection.
  arXiv  Detail & Related papers  (2026-05-06T16:35:47Z)
• RAFT-MSF++: Temporal Geometry-Motion Feature Fusion for Self-Supervised Monocular Scene Flow [51.43025173196566]
  Monocular scene flow estimation aims to recover dense 3D motion from image sequences.<n>We propose RAFT-MSF++, a self-supervised multi-frame framework that fuses temporal features to jointly estimate depth and scene flow.<n>Experiments show that RAFT-MSF++ achieves 24.14% SF-all on the KITTI Scene Flow benchmark, with a 30.99% improvement over the baseline and better robustness in occluded regions.
  arXiv  Detail & Related papers  (2026-04-21T11:32:49Z)
• SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing [43.39478017496301]
  Masked Autoencoder (MAE)-based pretraining stands out for its strong capability to learn general feature representations via reconstructing masked image regions.<n>Applying MAE to multispectral remote sensing images remains challenging due to complex backgrounds, indistinct targets, and the lack of semantic guidance during masking.<n> SIGMAE incorporates domain-specific spectral indices as prior knowledge to guide dynamic token masking toward informative regions.
  arXiv  Detail & Related papers  (2026-03-08T04:55:41Z)
• Multi-modal Deepfake Detection and Localization with FPN-Transformer [21.022230340898556]
  We introduce a multi-modal deepfake detection and localization framework based on a Feature Pyramid-Transformer (FPN-Transformer)<n>A multi-scale feature pyramid is constructed through R-TLM blocks with localized attention mechanisms, enabling joint analysis of cross-context temporal dependencies.<n>We evaluate our approach on the test set of the IJCAI'25 DDL-AV benchmark, showing a good performance with a final score of 0.7535.
  arXiv  Detail & Related papers  (2025-11-11T09:33:39Z)
• SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection [25.04992532067041]
  We propose the Spectral Cross-Attentional Network (SpecXNet), a dual-domain architecture for robust deepfake detection.<n>Built atop a modified XceptionNet backbone, we embed the DDFC and DFA modules within a separable convolution block.<n>Our results highlight the effectiveness of unified spatial-spectral learning for robust and general deepfake detection.
  arXiv  Detail & Related papers  (2025-09-26T08:51:59Z)
• 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)
• 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)
• Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts [57.01985221057047]
  This paper introduces a novel method that learnstemporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) Our method achieves state-of-theart performance on three public benchmarks for the WSVADL task.
  arXiv  Detail & Related papers  (2024-08-12T03:31:29Z)
• Delving into Sequential Patches for Deepfake Detection [64.19468088546743]
  Recent advances in face forgery techniques produce nearly untraceable deepfake videos, which could be leveraged with malicious intentions. Previous studies has identified the importance of local low-level cues and temporal information in pursuit to generalize well across deepfake methods. We propose the Local- & Temporal-aware Transformer-based Deepfake Detection framework, which adopts a local-to-global learning protocol.
  arXiv  Detail & Related papers  (2022-07-06T16:46:30Z)
• 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)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

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.