SPARK-IL reaches 94.6% mean accuracy on deepfake detection across 19 generators by fusing multi-band spectral embeddings from ViT and RGB paths, retrieving nearest signatures for majority voting, and using incremental learning with elastic weight consolidation.
arXiv preprint arXiv:2310.17419 (2024)
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Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
UniGenDet unifies generative and discriminative models through symbiotic self-attention and detector-guided alignment to co-evolve image generation and authenticity detection.
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SPARK-IL: Spectral Retrieval-Augmented RAG for Knowledge-driven Deepfake Detection via Incremental Learning
SPARK-IL reaches 94.6% mean accuracy on deepfake detection across 19 generators by fusing multi-band spectral embeddings from ViT and RGB paths, retrieving nearest signatures for majority voting, and using incremental learning with elastic weight consolidation.
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Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
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UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection
UniGenDet unifies generative and discriminative models through symbiotic self-attention and detector-guided alignment to co-evolve image generation and authenticity detection.