# ShieldGemma model card **Model Page** : [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma) **Resources and Technical Documentation**: - [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) - [ShieldGemma on Kaggle](https://www.kaggle.com/models/google/shieldgemma) - [ShieldGemma on Hugging Face Hub](https://huggingface.co/models?search=shieldgemma) **Terms of Use** : [Terms](https://ai.google.dev/gemma/terms) **Authors**: Google ## Model Information ShieldGemma 2 is a model trained on Gemma 3's 4B IT checkpoint for image safety classification across key categories that takes in images and outputs safety labels per policy. ### Description ShieldGemma 2, built on Gemma 3, is a 4 billion (4B) parameter model that checks the safety of both synthetic and natural images against key categories to help you build robust datasets and models. With this addition to the Gemma family of models, researchers and developers can now minimize the risk of harmful content affecting their Generative-AI powered applications across key [harm types](https://ai.google.dev/gemma/docs/shieldgemma/model_card_2#harm-types). We recommend using ShieldGemma 2 as an input filter to vision language models, or as an output filter of image generation systems. To train a robust image safety model, we curated training datasets of natural and synthetic images and instruction-tuned Gemma 3 to demonstrate strong performance. We compared safety policies to different benchmarks, and are releasing a technical report that incorporates third party benchmarks. ### Inputs and outputs - **Input:** Image and prompt instruction with policy definition. - **Output:** Probability of 'Yes' or 'No' tokens. A higher score for the 'Yes' token indicates that the model is confident that the content violates the included policy. The inverse is true for the 'No' token. Both scores are provided to enable classification metrics and calibration. #### Guidelines to use in prompts Here is our prompt instruction `[Prompt Instruction]`: You are a policy expert trying to help determine whether a generated or real image is in violation of the safety policy below. Your decision must very clearly follow the specific language used in the safety policy and should not include any interpretation outside of the safety policy. {policy} Does the image violate the above policy? Your Answer must start with 'Yes' or 'No'. **Use Case: Image Classification** | Harm Type | Guideline | |---|---| | Sexually Explicit content | "No Sexually Explicit content": The image shall not contain content that depicts explicit or graphic sexual acts (e.g., pornography, erotic. nudity, depictions of rape or sexual assault). | | Dangerous content | "No Dangerous content": The image shall not contain content that facilitates or encourages activities that could cause real-world harm (e.g., building firearms and explosive devices, promotion of terrorism, instructions for suicide). | | Violent content | "No Violence/Gore content": The image shall not contain content that depicts shocking, sensational, or gratuitous violence (e.g., excessive blood and gore, gratuitous violence against animals, extreme injury or moment of death). | ### Citation @misc{zeng2025shieldgemma2robusttractable, title={ShieldGemma 2: Robust and Tractable Image Content Moderation}, author={Wenjun Zeng and Dana Kurniawan and Ryan Mullins and Yuchi Liu and Tamoghna Saha and Dirichi Ike-Njoku and Jindong Gu and Yiwen Song and Cai Xu and Jingjing Zhou and Aparna Joshi and Shravan Dheep and Mani Malek and Hamid Palangi and Joon Baek and Rick Pereira and Karthik Narasimhan}, year={2025}, eprint={2504.01081}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2504.01081}, } ## Model Data Data used for model training and how the data was processed. ### Training Dataset Our training dataset consists of both natural images and synthetic images. For natural images, we sample a subset of images from WebLI (Web Language and Image) dataset that are relevant to the safety tasks. For synthetic images, we use an internal data generation pipeline to enable controlled generation of prompts and corresponding images that balance the diversity and severity of images. For this study, harm types were limited to dangerous, sexually explicit, and violent content, with English only. Additional adversarial and sub-topics were structured using a taxonomy that corresponds to respective policies, and a range of demographics, context, and regional aspects. ## Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: CSAM Filtering: CSAM (Child Sexual Abuse Material) filtering was applied in the data preparation process to ensure the exclusion of illegal content. ## Implementation Information ### Hardware ShieldGemma 2 was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e), for more details refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3). ### Software Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). For more details refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3). ## Evaluation ### Benchmark Results ShieldGemma 2 4B was evaluated against internal and external datasets. Our internal dataset is synthetically generated through our internal image data curation pipeline. This pipeline includes key steps such as problem definition, safety taxonomy generation, image query generation, image generation, attribute analysis, label quality validation, and more. We have approximately 500 examples for each harm policy. The positive ratios are 39%, 67%, 32% for sexual, dangerous content, violence respectively. We will also be releasing a technical report that includes evaluations against external datasets. **Internal Benchmark Evaluation Results** | Model | Sexually Explicit | Dangerous Content | Violence \& Gore | |---|---|---|---| | LlavaGuard 7B | 47.6/93.1/63.0 | 67.8/47.2/55.7 | 36.8/100.0/53.8 | | GPT-4o mini | 68.3/97.7/80.3 | 84.4/99.0/91.0 | 40.2/100.0/57.3 | | Gemma-3-4B-IT | 77.7/87.9/82.5 | 75.9/94.5/84.2 | 78.2/82.2/80.1 | | ShieldGemma-2-Image-4B | 87.6/89.7/88.6 | 95.6/91.9/93.7 | 80.3/90.4/85.0 | ## Ethics and Safety ### Evaluation Approach Although the ShieldGemma models are generative models, they are designed to be run in *scoring mode* to predict the probability that the next token would `Yes` or `No`. Therefore, safety evaluation focused primarily on outputting effective image safety labels. ### Evaluation Results These models were assessed for ethics, safety, and fairness considerations and met internal guidelines. When compared with benchmarks, evaluation datasets were iterated on and balanced against diverse taxonomies. Image safety labels were also human-labelled and checked for use cases that eluded the model, enabling us to improve upon rounds of evaluation. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage ShieldGemma 2 is intended to be used as a safety content moderator, either for human user inputs, model outputs, or both. These models are part of the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible), which is a set of recommendations, tools, datasets and models aimed to improve the safety of AI applications as part of the Gemma ecosystem. ### Limitations All the usual limitations for large language models apply, see the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3) for more details. Additionally, there are limited benchmarks that can be used to evaluate content moderation so the training and evaluation data might not be representative of real-world scenarios. ShieldGemma 2 is also highly sensitive to the specific user-provided description of safety principles, and might perform unpredictably under conditions that require a good understanding of language ambiguity and nuance. As with other models that are part of the Gemma ecosystem, ShieldGemma is subject to Google's [prohibited use policies](https://ai.google.dev/gemma/prohibited_use_policy). ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. We have carefully considered multiple aspects in the development of these models. Refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3) for more details. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have been shown to provide superior performance to other, comparably-sized open model alternatives.