SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914621686808576 |
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| author | Ma, Yingzi Liu, Xiaogeng Zheng, Yawen Xiao, Chaowei |
| author_facet | Ma, Yingzi Liu, Xiaogeng Zheng, Yawen Xiao, Chaowei |
| contents | With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos from a text prompt or an initial image. However, synthetic video generation -- especially when guided by an initial image -- often poses risks, including the potential creation of illegal, politically sensitive, or unethical content. Existing benchmarks have started to consider the safety of generated videos, but they primarily focus on testing models with malicious text prompts, ignoring the scenario where text prompt and image combination may still lead to harmful video content. In practice, this is a common and challenging issue: videos generated from safe text and image inputs can nonetheless convey harmful information. To bridge this gap, we introduce SafeGen-Bench, a benchmark specifically designed to evaluate the safety of conditional T2V models. Our benchmark defines 10 malicious categories, concentrating on risks related to both temporal sequences and depicted behaviors. SafeGen-Bench consists of carefully selected start frames from diverse image and video sources, paired with corresponding text prompts to simulate realistic inputs. We evaluate a variety of conditional T2V models on SafeGen-Bench, and the results indicate that current models struggle to consistently avoid generating malicious content with unsafety scores reaching up to 44.5, especially under conditions requiring high quality. Furthermore, we assess the effectiveness of both text-based and image-based guardrails on our benchmark, finding that unimodal guardrails alone were insufficient to provide a robust defense, with an 80\% failure rate across seven malicious categories. We hope that SafeGen-Bench will foster the development of safer and more controllable conditional T2V models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation Ma, Yingzi Liu, Xiaogeng Zheng, Yawen Xiao, Chaowei Computer Vision and Pattern Recognition With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos from a text prompt or an initial image. However, synthetic video generation -- especially when guided by an initial image -- often poses risks, including the potential creation of illegal, politically sensitive, or unethical content. Existing benchmarks have started to consider the safety of generated videos, but they primarily focus on testing models with malicious text prompts, ignoring the scenario where text prompt and image combination may still lead to harmful video content. In practice, this is a common and challenging issue: videos generated from safe text and image inputs can nonetheless convey harmful information. To bridge this gap, we introduce SafeGen-Bench, a benchmark specifically designed to evaluate the safety of conditional T2V models. Our benchmark defines 10 malicious categories, concentrating on risks related to both temporal sequences and depicted behaviors. SafeGen-Bench consists of carefully selected start frames from diverse image and video sources, paired with corresponding text prompts to simulate realistic inputs. We evaluate a variety of conditional T2V models on SafeGen-Bench, and the results indicate that current models struggle to consistently avoid generating malicious content with unsafety scores reaching up to 44.5, especially under conditions requiring high quality. Furthermore, we assess the effectiveness of both text-based and image-based guardrails on our benchmark, finding that unimodal guardrails alone were insufficient to provide a robust defense, with an 80\% failure rate across seven malicious categories. We hope that SafeGen-Bench will foster the development of safer and more controllable conditional T2V models. |
| title | SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.01481 |