רובוט מצטער, אדם מאושר: מודלים ויז'ואלי-לשוניים קוראים רק אחד משני שכבות טיפוגרפיות קריאות

תקציר מקורי באנגליתarXiv:2609.31403v1 Announce Type: new Abstract: Vision-language models (VLMs), despite their success in optical character recognition (OCR) tasks, are vulnerable to typographic attacks and have a fragile structure for images with multiple text layers. In this study, the DecoyBench dataset was created using the Decoy Font method. The dataset consists of 300 images, each containing text with sharp contour lines superimposed on another text with soft shading. Six recent closed-source models from three different model families were evaluated using this dataset under two different prompting conditions (naive and guided) and at two different resolutions ($512\times512$ and $64\times64$). A validation study showed that human participants could read both text layers with high accuracy. In contrast