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深度伪造:科学出版物中图像造假的新威胁?

Deepfakes: A new threat to image fabrication in scientific publications?

作者信息

Wang Liansheng, Zhou Lianyu, Yang Wenxian, Yu Rongshan

机构信息

Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China.

Aginome Scientific, Xiamen, China.

出版信息

Patterns (N Y). 2022 May 13;3(5):100509. doi: 10.1016/j.patter.2022.100509.

DOI:10.1016/j.patter.2022.100509
PMID:35607625
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9122956/
Abstract

There is an increasing risk of people using advanced artificial intelligence, particularly the generative adversarial network (GAN), for scientific image manipulation for the purpose of publications. We demonstrated this possibility by using GAN to fabricate several different types of biomedical images and discuss possible ways for the detection and prevention of such scientific misconducts in research communities.

摘要

人们使用先进的人工智能,尤其是生成对抗网络(GAN),用于为发表目的而进行科学图像操纵的风险正在增加。我们通过使用GAN伪造几种不同类型的生物医学图像来证明这种可能性,并讨论在研究社区中检测和预防此类科学不端行为的可能方法。

相似文献

1
Deepfakes: A new threat to image fabrication in scientific publications?深度伪造:科学出版物中图像造假的新威胁?
Patterns (N Y). 2022 May 13;3(5):100509. doi: 10.1016/j.patter.2022.100509.
2
AI-enabled image fraud in scientific publications.科学出版物中利用人工智能的图像造假。
Patterns (N Y). 2022 Jul 8;3(7):100511. doi: 10.1016/j.patter.2022.100511.
3
Fighting Deepfakes by Detecting GAN DCT Anomalies.通过检测生成对抗网络离散余弦变换异常来对抗深度伪造
J Imaging. 2021 Jul 30;7(8):128. doi: 10.3390/jimaging7080128.
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Audio deepfakes: A survey.音频深度伪造:一项调查。
Front Big Data. 2023 Jan 9;5:1001063. doi: 10.3389/fdata.2022.1001063. eCollection 2022.
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引用本文的文献

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Image fraud in nuclear medicine research.核医学研究中的图像造假。
Eur J Nucl Med Mol Imaging. 2025 Aug 16. doi: 10.1007/s00259-025-07515-5.
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AI detectors are poor western blot classifiers: a study of accuracy and predictive values.人工智能检测工具在蛋白质印迹法分类方面表现不佳:准确性和预测价值研究
PeerJ. 2025 Feb 20;13:e18988. doi: 10.7717/peerj.18988. eCollection 2025.
3
Exposing image splicing traces in scientific publications via uncertainty-guided refinement.通过不确定性引导的细化揭示科学出版物中的图像拼接痕迹。

本文引用的文献

1
The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications.生物医学研究出版物中不当图像重复的发生率
mBio. 2016 Jun 7;7(3):e00809-16. doi: 10.1128/mBio.00809-16.
2
Digital images are data: and should be treated as such.数字图像即数据,应作如此看待。
Methods Mol Biol. 2013;931:1-27. doi: 10.1007/978-1-62703-056-4_1.
Patterns (N Y). 2024 Aug 8;5(9):101038. doi: 10.1016/j.patter.2024.101038. eCollection 2024 Sep 13.
4
Experts fail to reliably detect AI-generated histological data.专家无法可靠地检测到 AI 生成的组织学数据。
Sci Rep. 2024 Nov 19;14(1):28677. doi: 10.1038/s41598-024-73913-8.
5
Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students.深度伪造视频可能带来的健康益处与风险:一项针对护理专业学生的定性研究
Nurs Rep. 2024 Oct 3;14(4):2746-2757. doi: 10.3390/nursrep14040203.
6
How to fight fake papers: a review on important information sources and steps towards solution of the problem.如何打击假论文:重要信息来源及解决问题步骤综述。
Naunyn Schmiedebergs Arch Pharmacol. 2024 Dec;397(12):9281-9294. doi: 10.1007/s00210-024-03272-8. Epub 2024 Jul 6.
7
Verification of nucleotide sequence reagent identities in original publications in high impact factor cancer research journals.在高影响力癌症研究期刊的原始出版物中验证核苷酸序列试剂的身份。
Naunyn Schmiedebergs Arch Pharmacol. 2024 Jul;397(7):5049-5066. doi: 10.1007/s00210-023-02846-2. Epub 2024 Jan 9.
8
How to stop AI deepfakes from sinking society - and science.如何阻止人工智能深度伪造技术侵蚀社会——以及科学。
Nature. 2023 Sep;621(7980):676-679. doi: 10.1038/d41586-023-02990-y.
9
Artificial intelligence in medicine and research - the good, the bad, and the ugly.医学与研究中的人工智能——利弊与挑战并存。
Saudi J Anaesth. 2023 Jul-Sep;17(3):401-406. doi: 10.4103/sja.sja_344_23. Epub 2023 Jun 22.
10
Protection of the human gene research literature from contract cheating organizations known as research paper mills.保护人类基因研究文献免受被称为论文工厂的合同作弊组织的侵害。
Nucleic Acids Res. 2022 Nov 28;50(21):12058-12070. doi: 10.1093/nar/gkac1139.