TruEye presents a mask-conditioned dual-stream transformer for fine-grained five-category detection and localization of AI-generated humans in images, claiming superior accuracy and speed over prior detectors on six datasets plus a new FineSyn dataset.
How good are humans at detecting ai-generated images? learnings from an experiment
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 5roles
background 2polarities
background 2representative citing papers
Gram-MMD is a texture-aware realism metric that computes MMD on upper-triangular Gram matrices from backbone activations, providing complementary information to semantic distributional metrics.
Users in r/isthisAI and r/RealOrAI employ 12 evolving strategies for AI detection that shift with model capabilities and online trends.
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.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
citing papers explorer
-
TruEye: Fine-Grained Detection of AI-Generated Human Subjects in Images
TruEye presents a mask-conditioned dual-stream transformer for fine-grained five-category detection and localization of AI-generated humans in images, claiming superior accuracy and speed over prior detectors on six datasets plus a new FineSyn dataset.
-
Gram-MMD: A Texture-Aware Metric for Image Realism Assessment
Gram-MMD is a texture-aware realism metric that computes MMD on upper-triangular Gram matrices from backbone activations, providing complementary information to semantic distributional metrics.
-
Is This AI? Longitudinal Analysis of Strategies Used for AI Detection on Two Subreddits
Users in r/isthisAI and r/RealOrAI employ 12 evolving strategies for AI detection that shift with model capabilities and online trends.
-
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
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.
-
From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.