TICS: text–image-based semantic CAPTCHA synthesis via multi-condition adversarial learning
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The generative adversarial learning paradigm has recently been widely adopted in multimodal arenas such as image captioning and image retrieval.
“In addition to its use in unimodal applications, the generative adversarial learning paradigm has recently been widely adopted in multimodal arenas, where two or more modalities are involved, such as image captioning [227] and image retrieval [228].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [227]
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References 39 · Cited by 13
- Authors
- Jia, X., Xiao, J., & Wu, C.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Wu, Chao
- Venue
- The Visual Computer
- Published
- 2021-02-09
- Volume
- 38
- Issue
- 3
- Pages
- 963-975
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3128591383
- Cited by
- 13
- References
- 39
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 2
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (LY19F020051)
- Fundamental Research Funds for the Artificial Intelligence Research Foundation of Baidu Inc.
- Fundamental Research Funds for the Zhejiang University and Cybervein Joint Research Lab (R19F020009)
- Fundamental Research Funds for the Zhejiang Natural Science Foundation (LZ17F020001)
- Fundamental Research Funds for the National Natural Science Foundation of China (61572-431)
- Fundamental Research Funds for National Natural Science Foundation of China (U19B2042)
- Fundamental Research Funds for the Key R&D Program of Zhejiang Province (2018C01006)
- Fundamental Research Funds for the Program of China Knowledge Center for Engineering Sciences and Technology
- Fundamental Research Funds for the Program of ZJU and Horizon Robotics Joint Research Lab
- Fundamental Research Funds for the Joint Research Program of ZJU and Hikvision Research Institute
- Fundamental Research Funds for the Major Scientific Research Project of Zhejiang Lab (No. 2018EC0ZX01-1)
- Fundamental Research Funds for the CAS Earth Science Research Project (XDA19020104)
- Topics
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- CAPTCHA, multimedia security, human-machine distinction, semantic reasoning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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