SafeScreen: A Safety-First Screening Framework for Personalized Video Retrieval for Vulnerable Users
- URL: http://arxiv.org/abs/2604.03264v1
- Date: Thu, 12 Mar 2026 21:28:10 GMT
- Title: SafeScreen: A Safety-First Screening Framework for Personalized Video Retrieval for Vulnerable Users
- Abstract summary: We introduce SafeScreen, a safety-first video screening framework that retrieves and presents personalized video.<n>Rather than ranking videos by relevance or popularity, SafeScreen treats safety as a prerequisite and performs sequential approval or rejection of candidate videos.<n>We evaluate SafeScreen in a dementia-care reminiscence case study using 30 synthetic patient profiles and 90 test queries.
- Score: 1.593065406609169
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Open-domain video platforms offer rich, personalized content that could support health, caregiving, and educational applications, but their engagement-optimized recommendation algorithms can expose vulnerable users to inappropriate or harmful material. These risks are especially acute in child-directed and care settings (e.g., dementia care), where content must satisfy individualized safety constraints before being shown. We introduce SafeScreen, a safety-first video screening framework that retrieves and presents personalized video while enforcing individualized safety constraints. Rather than ranking videos by relevance or popularity, SafeScreen treats safety as a prerequisite and performs sequential approval or rejection of candidate videos through an automated pipeline. SafeScreen integrates three key components: (i) profile-driven extraction of individualized safety criteria, (ii) evidence-grounded assessments via adaptive question generation and multimodal VideoRAG analysis, and (iii) LLM-based decision-making that verifies safety, appropriateness, and relevance before content exposure. This design enables explainable, real-time screening of uncurated video repositories without relying on precomputed safety labels. We evaluate SafeScreen in a dementia-care reminiscence case study using 30 synthetic patient profiles and 90 test queries. Results demonstrate that SafeScreen prioritizes safety over engagement, diverging from YouTube's engagement-optimized rankings in 80-93% of cases, while maintaining high levels of safety coverage, sensibleness, and groundedness, as validated by both LLM-based evaluation and domain experts.
Related papers
- Adaptive and Explicit safe: Triggering Latent Safety Awareness in Large Reasoning Models [31.940554924036807]
Large Reasoning Models (LRMs) remain highly vulnerable to sophisticated jailbreaks and direct harmful queries.<n>We employSupervised Fine-Tuning (SFT) to explicitly induce safe tags to trigger safety analysis and guidance.<n>We also apply Direct Preference Optimization (DPO) to further enhance the correctness and stability of the safety analysis and guidance.
arXiv Detail & Related papers (2026-06-15T14:51:34Z) - GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision [47.99880677909197]
GuardTrace-VL is a vision-aware safety auditor that monitors the full Question-Thinking-Answer (QTA) pipeline via joint image-text analysis.<n>We propose a three-stage progressive training scheme combined with the data refinement process, enabling the model to learn nuanced and context-dependent safety preferences.<n>On our proposed test set covering both in-domain and out-of-domain scenarios, GuardTrace-VL model achieves an F1 score of 93.1% on unsafe reasoning detection tasks.
arXiv Detail & Related papers (2025-11-26T02:49:51Z) - ConceptGuard: Proactive Safety in Text-and-Image-to-Video Generation through Multimodal Risk Detection [27.47621607462884]
ConceptGuard is a framework for proactively detecting and mitigating unsafe semantics in multimodal video generation.<n>A contrastive detection module identifies latent safety risks by projecting fused image-text inputs into a structured concept space.<n>A semantic suppression mechanism steers the generative process away from unsafe concepts by intervening in the prompt's multimodal conditioning.
arXiv Detail & Related papers (2025-11-24T05:27:05Z) - Rethinking Safety in LLM Fine-tuning: An Optimization Perspective [56.31306558218838]
We show that poor optimization choices, rather than inherent trade-offs, often cause safety problems, measured as harmful responses to adversarial prompts.<n>We propose a simple exponential moving average (EMA) momentum technique in parameter space that preserves safety performance.<n>Our experiments on the Llama families across multiple datasets demonstrate that safety problems can largely be avoided without specialized interventions.
arXiv Detail & Related papers (2025-08-17T23:46:36Z) - The Safety Reminder: A Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models [4.27794555931853]
Vision-Language Models (VLMs) face unique vulnerabilities due to their multimodal nature, allowing adversaries to bypass safety guardrails and trigger the generation of harmful content.<n>We propose The Safety Reminder'', a soft prompt tuning approach that optimize learnable prompt tokens, which are periodically injected during the text generation process to enhance safety awareness.
arXiv Detail & Related papers (2025-06-15T12:48:38Z) - HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model [58.12612140992874]
We introduce a holistic safety dataset and benchmark, textbfHoliSafe, that spans all five safe/unsafe image-text combinations.<n>We also propose a novel modular framework for enhancing VLM safety with a visual guard module (VGM) designed to assess the harmfulness of input images.<n> Experiments show that Safe-VLM with VGM, trained on our HoliSafe, achieves state-of-the-art safety performance across multiple VLM benchmarks.
arXiv Detail & Related papers (2025-06-05T07:26:34Z) - SafeVid: Toward Safety Aligned Video Large Multimodal Models [60.14535756294228]
We introduce SafeVid, a framework designed to instill video-specific safety principles in Video Large Multimodal Models (VLMMs)<n>SafeVid employs detailed textual video descriptions as an interpretive bridge, facilitating rule-driven safety reasoning.<n> Alignment with SafeVid-350K significantly enhances VLMM safety, with models like LLaVA-NeXT-Video demonstrating substantial improvements.
arXiv Detail & Related papers (2025-05-17T09:21:33Z) - Video-SafetyBench: A Benchmark for Safety Evaluation of Video LVLMs [45.265397990158846]
Video-SafetyBench is the first benchmark designed to evaluate the safety of LVLMs under video-text attacks.<n>It comprises 2,264 video-text pairs spanning 48 fine-grained unsafe categories.<n>To generate semantically accurate videos for safety evaluation, we design a controllable pipeline that decomposes video semantics into subject images and motion text.
arXiv Detail & Related papers (2025-05-17T05:06:38Z) - Safety Pretraining: Toward the Next Generation of Safe AI [68.99129474671282]
We present a data-centric pretraining framework that builds safety into the model from the start.<n>Our framework consists of four key steps: Safety Filtering, Safety Rephrasing, Native Refusal and Harmfulness-Tag annotated pretraining.<n>Our safety-pretrained models reduce attack success rates from 38.8% to 8.4% on standard LLM safety benchmarks with no performance on general degradation tasks.
arXiv Detail & Related papers (2025-04-23T17:58:08Z) - MLLM-as-a-Judge for Image Safety without Human Labeling [81.24707039432292]
In the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content.<n>It is crucial to identify such unsafe images based on established safety rules.<n>Existing approaches typically fine-tune MLLMs with human-labeled datasets.
arXiv Detail & Related papers (2024-12-31T00:06:04Z)
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.