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 gen- erated by large ai models: A survey
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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 30-minute training intervention increased US intelligence analysts' accuracy at distinguishing real from AI-generated images by 9 percentage points from a 72% baseline, mainly by improving identification of real images.
A systematic review of fully AI-generated image detection that organizes prior work around dataset construction and artifact extraction methods based on inductive priors.
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