When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Images

arXiv:2608.25933 · cs.CV, cs.AI · Submitted 2026-08-26 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-26

Updated: 2026-08-26

Comments: 6 pages, accepted at IEEE MMSP 2026

Code: https://github.com/Future-IQA/CO-AID

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes

Sources

• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
• How to Distinguish AI-Generated Images from Authentic Photographs

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