SpecSem-Net integrates Fourier-based spectral filtering with semantic-guided gated merging to detect AI-generated videos, reporting 87.25% accuracy on a new benchmark of five commercial generators and 95.59% on public datasets.
Tall: Thumbnail layout for deepfake video detection
3 Pith papers cite this work. Polarity classification is still indexing.
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SpInShield is a temporal spectral-invariant defense that decouples semantic motion from manipulatable spectral artifacts in deepfake detectors via a learnable adversary and shortcut suppression optimization.
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
citing papers explorer
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SpecSem-Net: Integrating Spectral and Semantic Features for Robust AI-generated Video Detection
SpecSem-Net integrates Fourier-based spectral filtering with semantic-guided gated merging to detect AI-generated videos, reporting 87.25% accuracy on a new benchmark of five commercial generators and 95.59% on public datasets.
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Exposing and Mitigating Temporal Attack in Deepfake Video Detection
SpInShield is a temporal spectral-invariant defense that decouples semantic motion from manipulatable spectral artifacts in deepfake detectors via a learnable adversary and shortcut suppression optimization.
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Detecting AI-Generated Videos with Spiking Neural Networks
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.