@inproceedings{ma-etal-2026-conceptguard,
title = "{C}oncept{G}uard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection",
author = "Ma, Ruize and
Cai, Minghong and
Jiang, Yilei and
Han, Jiaming and
Feng, Yi and
Tan, Yingshui and
Zhang, Bo and
Yue, Xiangyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2308/",
pages = "52445--52463",
ISBN = "979-8-89176-459-0",
abstract = "Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer greater controllability, they also introduce new safety risks, as harmful content can arise from individual modalities or their interaction. Existing safety methods are often limited to text-only inputs, require the user to manually specify the risk category in advance, or only audit content after generation, making them unable to proactively address compositional multimodal risks. We present ConceptGuard, a safeguard framework that proactively detects and mitigates unsafe semantics in multimodal video generation before harmful content is produced. ConceptGuard operates in two stages. First, a contrastive detection module fuses image and text inputs and computes their similarity against the risk concept set , automatically retrieving the top-k concepts most relevant to the input without requiring any prior assumption about the risk category. Second, a semantic suppression mechanism uses these identified concepts to remove unsafe semantics from both the text and the visual input during early generation steps. To support training and evaluation, we introduce ConceptRisk, a large-scale dataset for multimodal risk detection, and T2VSafetyBench-TI2V, the first benchmark for evaluating safety in Text-and-Image-to-Video generation. Experiments on both benchmarks show that ConceptGuard outperforms existing baselines in both risk detection and safe video generation."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ma-etal-2026-conceptguard">
<titleInfo>
<title>ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruize</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Minghong</namePart>
<namePart type="family">Cai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yilei</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiaming</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yingshui</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bo</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiangyu</namePart>
<namePart type="family">Yue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer greater controllability, they also introduce new safety risks, as harmful content can arise from individual modalities or their interaction. Existing safety methods are often limited to text-only inputs, require the user to manually specify the risk category in advance, or only audit content after generation, making them unable to proactively address compositional multimodal risks. We present ConceptGuard, a safeguard framework that proactively detects and mitigates unsafe semantics in multimodal video generation before harmful content is produced. ConceptGuard operates in two stages. First, a contrastive detection module fuses image and text inputs and computes their similarity against the risk concept set , automatically retrieving the top-k concepts most relevant to the input without requiring any prior assumption about the risk category. Second, a semantic suppression mechanism uses these identified concepts to remove unsafe semantics from both the text and the visual input during early generation steps. To support training and evaluation, we introduce ConceptRisk, a large-scale dataset for multimodal risk detection, and T2VSafetyBench-TI2V, the first benchmark for evaluating safety in Text-and-Image-to-Video generation. Experiments on both benchmarks show that ConceptGuard outperforms existing baselines in both risk detection and safe video generation.</abstract>
<identifier type="citekey">ma-etal-2026-conceptguard</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.2308/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>52445</start>
<end>52463</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection
%A Ma, Ruize
%A Cai, Minghong
%A Jiang, Yilei
%A Han, Jiaming
%A Feng, Yi
%A Tan, Yingshui
%A Zhang, Bo
%A Yue, Xiangyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-conceptguard
%X Recent progress in video generative models has enabled the creation of high-quality videos from multimodal prompts that combine text and images. While these systems offer greater controllability, they also introduce new safety risks, as harmful content can arise from individual modalities or their interaction. Existing safety methods are often limited to text-only inputs, require the user to manually specify the risk category in advance, or only audit content after generation, making them unable to proactively address compositional multimodal risks. We present ConceptGuard, a safeguard framework that proactively detects and mitigates unsafe semantics in multimodal video generation before harmful content is produced. ConceptGuard operates in two stages. First, a contrastive detection module fuses image and text inputs and computes their similarity against the risk concept set , automatically retrieving the top-k concepts most relevant to the input without requiring any prior assumption about the risk category. Second, a semantic suppression mechanism uses these identified concepts to remove unsafe semantics from both the text and the visual input during early generation steps. To support training and evaluation, we introduce ConceptRisk, a large-scale dataset for multimodal risk detection, and T2VSafetyBench-TI2V, the first benchmark for evaluating safety in Text-and-Image-to-Video generation. Experiments on both benchmarks show that ConceptGuard outperforms existing baselines in both risk detection and safe video generation.
%U https://aclanthology.org/2026.emnlp-main.2308/
%P 52445-52463
Markdown (Informal)
[ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection](https://aclanthology.org/2026.emnlp-main.2308/) (Ma et al., EMNLP 2026)
ACL
- Ruize Ma, Minghong Cai, Yilei Jiang, Jiaming Han, Yi Feng, Yingshui Tan, Bo Zhang, and Xiangyu Yue. 2026. ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 52445–52463, Budapest, Hungary. Association for Computational Linguistics.