🤖 AI Summary
Generative vision-language models (e.g., Stable Diffusion) excel in creative image synthesis but remain vulnerable to adversarial prompts that elicit unsafe, offensive, or culturally inappropriate outputs; existing defenses often compromise image quality or incur substantial computational overhead. This paper introduces VALOR—a modular, zero-shot proxy framework that enhances safety and utility in text-to-image generation via hierarchical prompt analysis and value-aligned reasoning. Its core contributions include: (i) integrated multi-level NSFW detection, cultural-value alignment, and intent disambiguation; (ii) LLM-driven selective prompt rewriting and optional stylistic regeneration; and (iii) semantics-preserving safe regeneration with dynamic role-instruction adaptation. Experiments demonstrate that VALOR achieves up to 100% suppression of unsafe outputs across diverse adversarial and culturally sensitive prompts, while preserving prompt fidelity, creativity, and functional utility.
📝 Abstract
Generative vision-language models like Stable Diffusion demonstrate remarkable capabilities in creative media synthesis, but they also pose substantial risks of producing unsafe, offensive, or culturally inappropriate content when prompted adversarially. Current defenses struggle to align outputs with human values without sacrificing generation quality or incurring high costs. To address these challenges, we introduce VALOR (Value-Aligned LLM-Overseen Rewriter), a modular, zero-shot agentic framework for safer and more helpful text-to-image generation. VALOR integrates layered prompt analysis with human-aligned value reasoning: a multi-level NSFW detector filters lexical and semantic risks; a cultural value alignment module identifies violations of social norms, legality, and representational ethics; and an intention disambiguator detects subtle or indirect unsafe implications. When unsafe content is detected, prompts are selectively rewritten by a large language model under dynamic, role-specific instructions designed to preserve user intent while enforcing alignment. If the generated image still fails a safety check, VALOR optionally performs a stylistic regeneration to steer the output toward a safer visual domain without altering core semantics. Experiments across adversarial, ambiguous, and value-sensitive prompts show that VALOR significantly reduces unsafe outputs by up to 100.00% while preserving prompt usefulness and creativity. These results highlight VALOR as a scalable and effective approach for deploying safe, aligned, and helpful image generation systems in open-world settings.