Cover Image

Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kang, Xiaolu, Wang, Zhongyuan, Cheng, Jikang, Huang, Baojin, Lei, Zhanhe, Wu, Gang, Zou, Qin, Wang, Qian
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tutor-Student Reinforcement Learning: A Dynamic Curriculum for Robust Deepfake Detection
by: Lei, Zhanhe, et al.
Published: (2026)

When Generative Replay Meets Evolving Deepfakes: Domain-Aware Relative Weighting for Incremental Face Forgery Detection
by: Shen, Hao, et al.
Published: (2025)

ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
by: Cheng, Jikang, et al.
Published: (2024)

Can We Leave Deepfake Data Behind in Training Deepfake Detector?
by: Cheng, Jikang, et al.
Published: (2024)

DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World
by: Cheng, Jikang, et al.
Published: (2024)

IDRetracor: Towards Visual Forensics Against Malicious Face Swapping
by: Cheng, Jikang, et al.
Published: (2024)

DC-Net: Divide-and-Conquer for Salient Object Detection
by: Zhu, Jiayi, et al.
Published: (2023)

Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection
by: Cheng, Jikang, et al.
Published: (2024)

DCDM: Divide-and-Conquer Diffusion Models for Consistency-Preserving Video Generation
by: Zhao, Haoyu, et al.
Published: (2026)

PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation
by: Xiong, Xinyu, et al.
Published: (2025)

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
by: Wang, Chong, et al.
Published: (2024)

DCA: Dividing and Conquering Amnesia in Incremental Object Detection
by: Zhang, Aoting, et al.
Published: (2025)

Evidential Deep Partial Multi-View Classification With Discount Fusion
by: Huang, Haojian, et al.
Published: (2024)

A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
by: Cheng, Jikang, et al.
Published: (2025)

Divide-and-Conquer Inference for Large-Scale Visual Recognition with Multimodal Large Language Models
by: Ye, Zhipeng, et al.
Published: (2026)

Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
by: Wang, Shijie, et al.
Published: (2026)

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning
by: Duan, Yue, et al.
Published: (2025)

DVPE: Divided View Position Embedding for Multi-View 3D Object Detection
by: Wang, Jiasen, et al.
Published: (2024)

Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting
by: Yan, Zhiying, et al.
Published: (2025)

Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery
by: Tu, Yuanpeng, et al.
Published: (2024)

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry
by: Le, Jiajun, et al.
Published: (2025)

Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation
by: Cong, Runmin, et al.
Published: (2025)

Divide and Conquer: Decoupled Representation Alignment for Multimodal World Models
by: Xiao, Junyuan, et al.
Published: (2026)

Visual Para-Thinker: Divide-and-Conquer Reasoning for Visual Comprehension
by: Xu, Haoran, et al.
Published: (2026)

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection
by: Tang, Hao, et al.
Published: (2024)

Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
by: Zou, Ke, et al.
Published: (2023)

UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
by: Dai, Tianjie, et al.
Published: (2023)

MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
by: Wang, Yanhui, et al.
Published: (2023)

Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation
by: Zhao, Xingyue, et al.
Published: (2025)

MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry
by: Cheng, Leo Kaixuan, et al.
Published: (2026)

Divide and Conquer: Multimodal Video Deepfake Detection via Cross-Modal Fusion and Localization
by: Li, Qingcao, et al.
Published: (2026)

Divide and Conquer Self-Supervised Learning for High-Content Imaging
by: Farndale, Lucas, et al.
Published: (2025)

Deepfake Detection: A Comprehensive Survey from the Reliability Perspective
by: Wang, Tianyi, et al.
Published: (2022)

Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification
by: Pei, Gaozheng, et al.
Published: (2025)

Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
by: Wang, Zhenyu, et al.
Published: (2024)

Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
by: Lin, Yu-Fan, et al.
Published: (2024)

Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection
by: Dai, Boyang, et al.
Published: (2026)

Gen3DSR: Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View
by: Ardelean, Andreea, et al.
Published: (2024)

Divide-and-Conquer: Tree-structured Strategy with Answer Distribution Estimator for Goal-Oriented Visual Dialogue
by: Cai, Shuo, et al.
Published: (2025)

Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble
by: Rolih, Blaž, et al.
Published: (2024)