Ethical-Lens: Curbing malicious usages of open-source text-to-image models.

Yuzhu Cai, Sheng Yin, Yuxi Wei, Chenxin Xu, Weibo Mao, Felix Juefei-Xu, Siheng Chen, Yanfeng Wang

Abstract read

In one paragraph

Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuzhu CaiSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Sheng YinSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Yuxi WeiSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Chenxin XuSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Weibo MaoSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Felix Juefei-XuNew York University, New York, NY 10012, USA.

Siheng ChenSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Yanfeng WangSchool of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200241, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALL·E 3, has revolutionized content creation across diverse sectors. However, these advances bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models such as DALL

Indexed as

AI safetylarge language modelstext-to-image modelsvalue alignment

Identifiers

PMID40182175

DOI10.1016/j.patter.2025.101187

PMCPMC11963081

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.