AI-generated content in a new 150K-post dataset spreads virally via passive engagement, reaches consensus faster once flagged, and evades detectors more effectively as models improve.
Detecting multimedia generated by large ai models: A survey
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
The XNote dataset and LVLM benchmarks demonstrate that current models face significant challenges in generating accurate, grounded Community Notes for image-based contextual deception.
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
Five universal physical descriptors including Laplacian variance, Sobel statistics, and residual noise variance, when integrated as text encodings with CLIP, achieve up to 99.8% accuracy detecting synthetic images across GAN and diffusion model datasets.
A multi-modal model with LMM semantic, ST visual, and PS audio branches enables simultaneous detection and fine-grained temporal localization of partial AI video forgeries, outperforming prior methods.
A systematic review of fully AI-generated image detection that organizes prior work around dataset construction and artifact extraction methods based on inductive priors.
citing papers explorer
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The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation
AI-generated content in a new 150K-post dataset spreads virally via passive engagement, reaches consensus faster once flagged, and evades detectors more effectively as models improve.
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XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception
The XNote dataset and LVLM benchmarks demonstrate that current models face significant challenges in generating accurate, grounded Community Notes for image-based contextual deception.
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VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
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Deepfake Detection Generalization with Diffusion Noise
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
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Beyond Semantics: Uncovering the Physics of Fakes via Universal Physical Descriptors for Cross-Modal Synthetic Detection
Five universal physical descriptors including Laplacian variance, Sobel statistics, and residual noise variance, when integrated as text encodings with CLIP, achieve up to 99.8% accuracy detecting synthetic images across GAN and diffusion model datasets.
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Towards multi-modal forgery representation learning for AI-generated video detection and localization
A multi-modal model with LMM semantic, ST visual, and PS audio branches enables simultaneous detection and fine-grained temporal localization of partial AI video forgeries, outperforming prior methods.
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Fully AI-Generated Image Detection: Definition, Recent Advances and Challenges
A systematic review of fully AI-generated image detection that organizes prior work around dataset construction and artifact extraction methods based on inductive priors.