← Back to all news
arXiv Computer Vision By Zeyu Zhang, Dingyi Rong, Zijian Chen, Zicheng Zhang, Xiongkuo Min, Guangtao Zhai

Invisible in Space, Visible in Time: Motion Vision CAPTCHA against GUI Agents

Read the original on arXiv Computer Vision →

The paper introduces Motion Vision CAPTCHA (MVCAP), a new CAPTCHA framework that relies on motion-defined foreground structures to create challenges that are only solvable through temporal analysis of a dynamic background. MVCAP is implemented in three progressive levels—coherent motion, structural motion, and biological motion—and evaluated using the MVCAP-Bench, a browser-based benchmark with 600 live CAPTCHA instances. Human participants achieve 99.6% accuracy, whereas the best GUI agent scores only 16.8%, highlighting a significant human–agent perception gap and demonstrating that dynamic background camouflage is the key difficulty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Jun 30

CAPTCHA Solving for Native GUI Agents: Automated Reasoning-Action Data Generation and Self-Corrective Training

arXiv:2603. 23559v2 Announce Type: replace-cross Abstract: GUI agents are rapidly shifting from multi-module pipelines to end-to-end, native vision-language models (VLMs) that perceive raw screenshots and directly interact with digital devices.

By Yuxi Chen, Haoyu Zhai, Chenkai Wang, Rui Yang, Lingming Zhang, Gang Wang, Huan Zhang
llmsagentsmultimodalbenchmarks
More like this →
arXiv AI
Jun 2

HLL: Can Agents Cross Humanity's Last Line of Verification?

arXiv:2606. 02449v1 Announce Type: new Abstract: Multimodal agents are increasingly expected to operate interfaces on behalf of users, raising a central deployment question: can they truly substitute for humans in workflows that services deliberately protect against automation?

By Xinhao Song, Su Su, Sirui Song, Hongliang Wu, Wen Shen, Zhihua Wei, Gongshen Liu, Linfeng Zhang, Dongrui Liu
agentsmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 10

VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models

arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...

By Zaid Pervaiz Bhat, Nimra Nayyar, Arihant Jain, Lap Fung Chan, John Suchanek, Yu Wang, Varun Praveen, Tomasz Kornuta, Vidya Nariyambut Murali
llmsnlproboticsmultimodalbenchmarkssafety
More like this →
arXiv AI
Jun 9

MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention

arXiv:2606. 07639v1 Announce Type: cross Abstract: Video understanding is shifting from the offline paradigm -- taking a fully recorded video as input and producing a single answer after it ends -- toward real-time interaction, in which the model perceives new frames while still replying, revises its answer as new evidence appears, and remains silent when there is nothing to say.

By Pengyu Wang, Chenkun Tan, Shaojun Zhou, Wei Huang, Qirui Zhou, Zhan Huang, Zhen Ye, Jijun Cheng, Xiaomeng Qian, Yanxin Chen, Xingyang He, Huazheng Zeng, Chenghao Wang, Pengfei Wang, Hongkai Wang, Shanqing Gao, Yixian Tian, Chenghao Liu, Xinghao Wang, Botian Jiang, Xipeng Qiu
efficiencymultimodal
More like this →