New experimental AI tool helps people explore the context and origin of images seen online.
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A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
The paper introduces the CO-AID dataset, which captures systematic defects in state‑of‑the‑art text‑to‑image models when prompts involve complex composition such as multiple entities and attributes. Researchers manually curated 651 reference images across people, hand, object, and scene categories, edited ChatGPT‑generated prompts to emphasize compositional factors, and generated AI images with three T2I models. A subjective study with 29 participants produced multi‑label defect annotations, enabling training of a deep model that predicts defects and improves image generation.
Our new paper analyzes the important ways AI systems organize the visual world differently from humans.
arXiv:2408. 00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content.
arXiv:2606. 28510v1 Announce Type: cross Abstract: Across social and online platforms, people are increasingly exposed to AI-generated images.
OpenAI advances AI content provenance with Content Credentials, SynthID, and a verification tool to help people identify and trust AI-generated media.
