Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
Deepfake video detection using convolutional vision transformer
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
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cs.CV 4years
2026 4representative citing papers
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.
citing papers explorer
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Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis
Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
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Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.
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LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
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MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
MFVLR uses multi-domain vision-language reconstruction with a fine-grained language transformer, multi-domain vision encoder, and vision injection module to achieve generalizable detection and localization of diffusion-synthesized face forgeries.