人工智能生成艺术图像的信任研究

陈莉, 刘艺东, 王康, 陈欣星

包装工程(设计栏目) ›› 2025, Vol. 46 ›› Issue (18) : 359-367.

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包装工程(设计栏目) ›› 2025, Vol. 46 ›› Issue (18) : 359-367. DOI: 10.19554/j.cnki.1001-3563.2025.18.032
设计研讨

人工智能生成艺术图像的信任研究

  • 陈莉1, 刘艺东2,*, 王康3, 陈欣星4
作者信息 +

Trust Study on Artificial Intelligence Generated Artistic Images

  • CHEN Li1, LIU Yidong2,*, WANG Kang3, CHEN Xinxing4
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文章历史 +

摘要

目的 人工智能生成的艺术图像在包装设计领域已有大量实践案例,但并非所有AI生成的图像都能被用户接受。探索艺术图像的属性如何影响人们判断艺术图像是否由AI生成,重点不在于用户是否分辨出了AI生成的艺术图像,而在于用户是否“信任”图像的绘制方式,以此为设计师筛选AI生成的艺术图像及AI生成图像技术发展提供实证参考和建议。方法 基于艺术图像的复杂度、样本熵和艺术风格3种属性分类艺术图像数据库中AI生成艺术图像与真人绘制艺术图像;筛选160张不同复杂度、艺术风格的艺术图像,参与者对每幅艺术图像是否由AI生成的信任程度进行打分;对参与者进行艺术知识水平问卷测试;分析艺术图像属性和参与者对图像绘制方式的信任程度。结果 熵对于参与者信任没有显著影响,复杂度和个体艺术知识水平对于参与者信任图像的绘制方式有显著影响。结论 研究结果对于理解艺术图像的用户信任构建具有实际意义,加深了对图像属性如何影响人类感知的理解。

Abstract

There have been numerous practical cases of AI-generated art images in the field of packaging design, but not all AI-generated images are accepted by users. The work aims to explore how the properties of art images influence people's judgment about whether art images are generated by AI, with focus on whether the user can "trust" the way the image is drawn, rather than whether the user can distinguish the AI-generated art image, so as to provide empirical reference and advice for designers to screen the AI-generated art image and AI-generated image technology. AI generated art images and real-life art images in the art image database were classified based on the three attributes of art image complexity, sample entropy and art style; 160 art images with different complexity and art style were screened. Each art image was scored by the subjects based on their level of trust in whether it was generated by AI. A questionnaire test was conducted on the artistic knowledge level of the subjects. The attributes of art images and the users' degree of trust on the ways of image drawing were analyzed. The results showed that entropy had no significant effect on user trust, and complexity and individual artistic knowledge had a significant effect on the way users trust images. The results have practical implications for understanding user trust building in artistic images, and deepen the understanding of how image properties affect human perception.

关键词

信任 / 艺术图像设计 / 人工智能生成 / 复杂度 / 视觉认知

Key words

trust / art image design / artificial intelligence generation / complexity / visual recognition

引用本文

导出引用1
陈莉, 刘艺东, 王康, 陈欣星. 人工智能生成艺术图像的信任研究[J]. 包装工程. 2025, 46(18): 359-367 https://doi.org/10.19554/j.cnki.1001-3563.2025.18.032
CHEN Li, LIU Yidong, WANG Kang, CHEN Xinxing. Trust Study on Artificial Intelligence Generated Artistic Images[J]. Packaging Engineering. 2025, 46(18): 359-367 https://doi.org/10.19554/j.cnki.1001-3563.2025.18.032
中图分类号: TB482   

参考文献

[1] KELLY S, KAYE S A, OVIEDO-TRESPALACIOS O.What Factors Contribute to the Acceptance of Artificial Intelligence? A Systematic Review[J]. Telematics and Informatics, 2023, 77: 101925.
[2] CHI O H, JIA S Z, LI Y F, et al.Developing a Formative Scale to Measure Consumers' Trust Toward Interaction with Artificially Intelligent (AI) Social Robots in Service Delivery[J]. Computers in Human Behavior, 2021, 118: 106700.
[3] 蒋昀霖, 夏志杰, 谢妍曦. 人工智能辅助辨别虚假信息中的用户信任与感知研究[J]. 情报杂志, 2025, 44(1): 164-171.
JIANG Y L, XIA Z J, XIE Y X.Research on Trust and Perception in Artificial Intelligence-Assisted User Identification of Disinformation[J]. Journal of Intelligence, 2025, 44(1): 164-171.
[4] 齐玥, 陈俊廷, 秦邵天, 等. 通用人工智能时代的人与AI信任[J]. 心理科学进展, 2024, 32(12): 2124-2136.
QI Y, CHEN J T, QIN S T, et al.Human-AI Mutual Trust in the Era of Artificial General Intelligence[J]. Advances in Psychological Science, 2024, 32(12): 2124-2136.
[5] GILLATH O, AI T, BRANICKY M S, et al.Attachment and Trust in Artificial Intelligence[J]. Computers in Human Behavior, 2021, 115: 106607.
[6] GLIKSON E, WOOLLEY A W.Human Trust in Artificial Intelligence: Review of Empirical Research[J]. Academy of Management Annals, 2020, 14(2): 627-660.
[7] 徐娟芳, 陈子昂. 基于eHMI的车外人机交互信任影响因素研究[J]. 包装工程, 2024, 45(14): 225-232.
XU J F, CHEN Z A.Influencing Factor of Trust in External Human-Machine Interface[J]. Packaging Engineering, 2024, 45(14): 225-232.
[8] 孙造诣, 许苇婧, 陈思恬, 等. 算法还是专家? 任务类型对人机信任和价值感知的影响[J]. 包装工程, 2023, 44(20): 18-24.
SUN Z Y, XU W J, CHEN S T, et al.Algorithm or Human Expert? Effects of Task Type on Human-Computer Trust and Perception of Value[J]. Packaging Engineering, 2023, 44(20): 18-24.
[9] 周美玉, 李超, 帅如. 以信任为导向的跨境电商服务设计模型[J]. 包装工程, 2019, 40(16): 101-107.
ZHOU M Y, LI C, SHUAI R.A Service Design Model of Cross-Border E-Commerce Based on Trust[J]. Packaging Engineering, 2019, 40(16): 101-107.
[10] 何人可, 韩欣雨. 通过用户体验设计提升网站初始信任的研究[J]. 包装工程, 2015, 36(22): 5-8.
HE R K, HAN X Y.Enhancing the Website’s Initial Trust through User Experience Design[J]. Packaging Engineering, 2015, 36(22): 5-8.
[11] MA X Y, HUO Y D.Drawing a Satisfying Picture: An Exploratory Study of Human-AI Interaction in AI Painting through Breakdown-Repair Communication Strategies[J]. Information Processing & Management, 2024, 61(4): 103755.
[12] KAPLAN A D, KESSLER T T, CHRISTOPHER BRILL J, et al.Trust in Artificial Intelligence: Meta-Analytic Findings[J]. Human Factors, 2023, 65(2): 337-359.
[13] GANGADHARBATLA H.The Role of AI Attribution Knowledge in the Evaluation of Artwork[J]. Empirical Studies of the Arts, 2022, 40(2): 125-142.
[14] PARK J, KANG H, KIM H Y.Human, Do You Think this Painting Is the Work of a Real Artist?[J]. International Journal of Human-Computer Interaction, 2024, 40(18): 5174-5191.
[15] OMRANI N, RIVIECCIO G, FIORE U, et al.To Trust or Not to Trust? An Assessment of Trust in AI-Based Systems: Concerns, Ethics and Contexts[J]. Technological Forecasting and Social Change, 2022, 181: 121763.
[16] CHOUNG H, DAVID P, ROSS A.Trust in AI and Its Role in the Acceptance of AI Technologies[J]. International Journal of Human-Computer Interaction, 2023, 39(9): 1727-1739.
[17] WANG Z, BOVIK A C, SHEIKH H R, et al.Image Quality Assessment: From Error Visibility to Structural Similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[18] ZHANG R, ISOLA P, EFROS A A, et al.The Unreasonable Effectiveness of Deep Features as a Perceptual Metric[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 586-595.
[19] SANAKOYEU A, KOTOVENKO D, LANG S, et al.A Style-Aware Content Loss for Real-Time HD Style Transfer[M]. Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 715-731.
[20] CHEN J Y, AN J, LYU H J, et al.Learning to Evaluate the Artness of AI-Generated Images[J]. IEEE Transactions on Multimedia, 2024, 26: 10731-10740.
[21] CHEIN J M, MARTINEZ S A, BARONE A R.Human Intelligence Can Safeguard Against Artificial Intelligence: Individual Differences in the Discernment of Human from AI Texts[J]. Scientific Reports, 2024, 14(1): 25989.
[22] SUN Y K, YANG C H, LYU Y R, et al.From Pigments to Pixels: A Comparison of Human and AI Painting[J]. Applied Sciences, 2022, 12(8): 3724.
[23] TAYLOR R P, GUZMAN R, MARTIN T P, et al.Authenticating Pollock Paintings Using Fractal Geometry[J]. Pattern Recognition Letters, 2007, 28(6): 695-702.
[24] KIM D, SON S W, JEONG H.Large-Scale Quantitative Analysis of Painting Arts[J]. Scientific Reports, 2014, 4: 7370.
[25] ZUJOVIC J, GANDY L, FRIEDMAN S, et al.Classifying Paintings by Artistic Genre: An Analysis of Features & Classifiers[C]//2009 IEEE International Workshop on Multimedia Signal Processing. Rio de Janeiro: IEEE, 2009: 1-5.
[26] PERC M.Beauty in Artistic Expressions through the Eyes of Networks and Physics[J]. Journal of the Royal Society, Interface, 2020, 17(164): 20190686.
[27] TAYLOR R P, MICOLICH A P, JONAS D.Fractal Analysis of Pollock's Drip Paintings[J]. Nature, 1999, 399: 422.
[28] MUREIKA J R, FAIRBANKS M S, TAYLOR R P.Multifractal Comparison of the Painting Techniques of Adults and Children[C]//Computer Vision and Image Analysis of Art. San Jose: SPIE, 2010: 191-196.
[29] LEE B, KIM D, SUN S, et al.Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set[J]. PLoS One, 2018, 13(9): e0204430.
[30] PEPTENATU D, ANDRONACHE I, AHAMMER H, et al.Kolmogorov Compression Complexity may Differentiate Different Schools of Orthodox Iconography[J]. Scientific Reports, 2022, 12(1): 10743.
[31] SIGAKI H Y D, PERC M, RIBEIRO H V. History of Art Paintings through the Lens of Entropy and Complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(37): E8585-E8594.
[32] PAPIA E M, KONDI A, CONSTANTOUDIS V.Entropy and Complexity Analysis of AI-Generated and Human- Made Paintings[J]. Chaos, Solitons & Fractals, 2023, 170: 113385.
[33] BELFI A M, VESSEL E A, BRIELMANN A, et al.Dynamics of Aesthetic Experience Are Reflected in the Default-Mode Network[J]. NeuroImage, 2019, 188: 584-597.
[34] STANISCHEWSKI S, ALTMANN C S, BRACHMANN A, et al.Aesthetic Perception of Line Patterns: Effect of Edge-Orientation Entropy and Curvilinear Shape[J]. i-Perception, 2020, 11(5): 2041669520950749.
[35] HEAPS C, HANDEL S.Similarity and Features of Natural Textures[J]. Journal of Experimental Psychology: Human Perception and Performance, 1999, 25(2): 299-320.
[36] DENG L Q, POOLE M S.Aesthetic Design of E- Commerce Web Pages-Webpage Complexity, Order and Preference[J]. Electronic Commerce Research and Applications, 2012, 11(4): 420-440.
[37] HARPER S, MICHAILIDOU E, STEVENS R.Toward a Definition of Visual Complexity as an Implicit Measure of Cognitive Load[J]. ACM Transactions on Applied Perception, 2009, 6(2): 1-18.
[38] GU Z Y, JIN C H, CHANG D, et al.Predicting Webpage Aesthetics with Heatmap Entropy[J]. Behaviour & Information Technology, 2021, 40(7): 676-690.
[39] WANG Z Y, DUFF B R L, CLAYTON R B. Establishing a Factor Model for Aesthetic Preference for Visual Complexity of Brand Logo[J]. Journal of Current Issues & Research in Advertising, 2018, 39(1): 83-100.
[40] LANDWEHR J R, LABROO A A, HERRMANN A.Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts[J]. Marketing Science, 2011, 30(3): 416-429.
[41] CARDACI M, DI GESÙ V, PETROU M, et al.A Fuzzy Approach to the Evaluation of Image Complexity[J]. Fuzzy Sets and Systems, 2009, 160(10): 1474-1484.
[42] ROSENHOLTZ R, LI Y Z, NAKANO L.Measuring Visual Clutter[J]. Journal of Vision, 2007, 7(2): 17.
[43] CORCHS S E, CIOCCA G, BRICOLO E, et al.Predicting Complexity Perception of Real World Images[J]. PLoS One, 2016, 11(6): e0157986.
[44] HARDY, RAND &, CATHERINE. The Ishihara Test as a Means of Detecting and Analyzing Defective Color Vision[J]. The Journal of General Psychology, 1947, 36(1): 79-106.
[45] SHANNON C E.A Mathematical Theory of Communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423.
[46] GONZALEZ R C.Digital Image Processing[M]. Berlin: Pearson Education India, 2009.
[47] LOPEZ-RUIZ R, MANCINI H L, CALBET X.A Statistical Measure of Complexity[J]. Physics Letters A, 1995, 209(5/6): 321-326.
[48] CHIRUMBOLO A, BRIZI A, MASTANDREA S, et al.‘Beauty Is No Quality in Things Themselves’: Epistemic Motivation Affects Implicit Preferences for Art[J]. PLoS One, 2014, 9(10): e110323.
[49] BELKE B, LEDER H, AUGUSTIN D. Mastering Style.Effects of Explicit Style-Related Information, Art Knowledge and Affective State on Appreciation of Abstract Paintings[J]. Psychology Science, 2006, 48(2): 115.
[50] BRIEBER D, LEDER H, NADAL M.The Experience of Art in Museums: An Attempt to Dissociate the Role of Physical Context and Genuineness[J]. Empirical Studies of the Arts, 2015, 33(1): 95-105.
[51] SPECKER E, FORSTER M, BRINKMANN H, et al.The Vienna Art Interest and Art Knowledge Questionnaire (VAIAK): A Unified and Validated Measure of Art Interest and Art Knowledge[J]. Psychology of Aesthetics, Creativity, and the Arts, 2020, 14(2): 172-185.
[52] JEBB A T, NG V, TAY L.A Review of Key Likert Scale Development Advances: 1995-2019[J]. Frontiers in Psychology, 2021, 12: 637547.
[53] RYAN G, MOSCA A, CHANG R, et al.At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1): 872-881.
[54] SHI Y, GAO T, JIAO X H, et al.Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature Review[J]. Proceedings of the ACM on Human-Computer Interaction, 2023, 7(2): 1-35.

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国家自然科学基金青年基金(62103180)

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