REVIEW 7 cited by
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures are typically limited to text-based filtering or concept removal strategies, able to remove just a few concepts from the model's generative capabilities. In this work, we introduce AlignGuard, a method for safety alignment of T2I models. We enable the application of Direct Preference Optimization (DPO) for safety purposes in T2I models by synthetically generating a dataset of harmful and safe image-text pairs, which we call CoProV2. Using a custom DPO strategy and this dataset, we train safety experts, in the form of low-rank adaptation (LoRA) matrices, able to guide the generation process away from specific safety-related concepts. Then, we merge the experts into a single LoRA using a novel merging strategy for optimal scaling performance. This expert-based approach enables scalability, allowing us to remove 7x more harmful concepts from T2I models compared to baselines. AlignGuard consistently outperforms the state-of-the-art on many benchmarks and establishes new practices for safety alignment in T2I networks. Code and data will be shared at https://safetydpo.github.io/.
Forward citations
Cited by 7 Pith papers
-
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
UniNDM detects sexual intent from early-stage diffusion noise and mitigates it via LLM-generated negative prompts and initial-noise optimization, across U-Net and DiT models.
-
Minimalist Concept Erasure in Generative Models
A final-output-only loss with learned neuron masks erases concepts from flow-based image generators more robustly than per-step fine-tuning methods.
-
"I Cannot Write This Because It Violates Our Content Policy": Understanding Content Moderation Policies and User Experiences in Generative AI Products
GAI tools' content moderation policies are comprehensive in scope but thin on user reporting and appeals, and Reddit users report frequent frustration with opaque moderation decisions.
-
Red-Teaming Text-to-Image Systems by Rule-based Preference Modeling
RPG-RT iteratively fine-tunes an LLM with rule-based preferences from a decoupled CLIP scoring model, letting it rewrite prompts that bypass unknown safety defenses in black-box text-to-image systems.
-
From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
An explainable model using BLEURT, CometKiwi, pause features, and Chinese phraseological diversity predicts human-rated quality dimensions in English-Chinese consecutive interpreting, with SHAP identifying the stronge...
-
Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
A hierarchical direct preference optimization with four alignment levels plus automated data selection improves physical plausibility of text-to-video models.
-
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
This survey classifies concept erasure methods for text-to-image diffusion models along intervention level, optimization strategy, and semantic scope, and reviews the datasets, metrics, and benchmarks used to evaluate them.
Discussion (0). Sign in to comment.