LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

• URL: http://arxiv.org/abs/2406.05113v2
• Date: Fri, 31 Jan 2025 15:57:48 GMT
• Title: LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models
• Authors: Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski,
• Abstract summary: LlavaGuard is a suite of VLM-based vision safeguards that address the critical need for reliable guardrails in the era of large-scale data and models. We establish a novel open framework, describing a customizable safety taxonomy, data preprocessing, augmentation, and training setup. We demonstrate LlavaGuard's performance in two real-world applications: large-scale dataset annotation and moderation of text-to-image models.
• Score: 26.148022772521493
• License:
• Abstract: This paper introduces LlavaGuard, a suite of VLM-based vision safeguards that address the critical need for reliable guardrails in the era of large-scale data and models. To this end, we establish a novel open framework, describing a customizable safety taxonomy, data preprocessing, augmentation, and training setup. For teaching a VLM safeguard on safety, we further create a multimodal safety dataset with high-quality human expert annotations, where each image is labeled with a safety rating, category and rationale. We also employ advanced augmentations to support context-specific assessments. The resulting LlavaGuard models, ranging from 0.5B to 7B, serve as a versatile tool for evaluating the safety compliance of visual content against flexible policies. In comprehensive experiments, LlavaGuard outperforms both state-of-the-art safeguards and VLMs in accuracy and in flexibly handling different policies. Additionally, we demonstrate LlavaGuard's performance in two real-world applications: large-scale dataset annotation and moderation of text-to-image models. We make our entire framework publicly available, including the dataset and model weights.

Related papers

• Safety at Scale: A Comprehensive Survey of Large Model Safety [299.801463557549]
  We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
  arXiv  Detail & Related papers  (2025-02-02T05:14:22Z)
• Retention Score: Quantifying Jailbreak Risks for Vision Language Models [60.48306899271866]
  Vision-Language Models (VLMs) are integrated with Large Language Models (LLMs) to enhance multi-modal machine learning capabilities. This paper aims to assess the resilience of VLMs against jailbreak attacks that can compromise model safety compliance and result in harmful outputs. To evaluate a VLM's ability to maintain its robustness against adversarial input perturbations, we propose a novel metric called the textbfRetention Score.
  arXiv  Detail & Related papers  (2024-12-23T13:05:51Z)
• Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios [32.16984263644299]
  Large Language Models (LLMs) can generate valuable data for safety measures, but often exhibit distributional biases. We propose a novel framework that integrates active learning with clustering to guide LLM generation. Our results show that the proposed framework produces a more representative set of safety scenarios without requiring prior knowledge of the underlying data distribution.
  arXiv  Detail & Related papers  (2024-10-14T21:48:14Z)
• Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning [54.30994558765057]
  The study pioneers a comprehensive privacy protection framework that safeguards image data privacy concurrently during data sharing and model publication. We propose an interactive image privacy protection framework that utilizes generative machine learning models to modify image information at the attribute level. Within this framework, we instantiate two modules: a differential privacy diffusion model for protecting attribute information in images and a feature unlearning algorithm for efficient updates of the trained model on the revised image dataset.
  arXiv  Detail & Related papers  (2024-09-05T07:55:55Z)
• ShieldGemma: Generative AI Content Moderation Based on Gemma [49.91147965876678]
  ShieldGemma is a suite of safety content moderation models built upon Gemma2. Models provide robust, state-of-the-art predictions of safety risks across key harm types.
  arXiv  Detail & Related papers  (2024-07-31T17:48:14Z)
• Safety Alignment for Vision Language Models [21.441662865727448]
  We enhance the visual modality safety alignment of Vision Language Models (VLMs) by adding safety modules. Our method boasts ease of use, high flexibility, and strong controllability, and it enhances safety while having minimal impact on the model's general performance.
  arXiv  Detail & Related papers  (2024-05-22T12:21:27Z)
• Latent Guard: a Safety Framework for Text-to-image Generation [64.49596711025993]
  Existing safety measures are either based on text blacklists, which can be easily circumvented, or harmful content classification. We propose Latent Guard, a framework designed to improve safety measures in text-to-image generation. Inspired by blacklist-based approaches, Latent Guard learns a latent space on top of the T2I model's text encoder, where it is possible to check the presence of harmful concepts.
  arXiv  Detail & Related papers  (2024-04-11T17:59:52Z)
• AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts [0.0]
  As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase. We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas. To address this, we define a broad content safety risk taxonomy, comprising 13 critical risk and 9 sparse risk categories.
  arXiv  Detail & Related papers  (2024-04-09T03:54:28Z)
• Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models [39.56233272612982]
  Current vision large language models (VLLMs) exhibit remarkable capabilities yet are prone to generate harmful content and are vulnerable to jailbreaking attacks. Our initial analysis finds that this is due to the presence of harmful data during vision-language instruction fine-tuning. To address this issue, we first curate a vision-language safe instruction-following dataset VLGuard covering various harmful categories.
  arXiv  Detail & Related papers  (2024-02-03T16:43:42Z)
• How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs [55.91371032213854]
  This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. We introduce a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
  arXiv  Detail & Related papers  (2023-11-27T18:59:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.