MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
Dimodif: Discourse modality-information differentiation for audio- visual deepfake detection and localization
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
IaMSB applies a Schrödinger Bridge in two stages to estimate cross-modal consistency and localize deepfake intervals, reporting 3-10% gains in AP@0.95 especially on single-sided forgeries.
AVPF generates self-created audio-visual pseudo-fakes from real samples to train deepfake detectors that generalize better, with reported average gains up to 7.4%.
citing papers explorer
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MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
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Inconsistency-aware Multimodal Schr\"odinger Bridge for Deepfake Localization
IaMSB applies a Schrödinger Bridge in two stages to estimate cross-modal consistency and localize deepfake intervals, reporting 3-10% gains in AP@0.95 especially on single-sided forgeries.
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Generalizing Video DeepFake Detection by Self-generated Audio-Visual Pseudo-Fakes
AVPF generates self-created audio-visual pseudo-fakes from real samples to train deepfake detectors that generalize better, with reported average gains up to 7.4%.