基于显著性特征的海报设计侵权检测分析

doi:10.6040/j.issn.1671-9352.9.2025.003

《山东大学学报(理学版)》 ›› 2026, Vol. 61 ›› Issue (3): 11-19.doi: 10.6040/j.issn.1671-9352.9.2025.003

• • 上一篇

基于显著性特征的海报设计侵权检测分析

杨滨¹,孙建楠¹,曹恩国^1*,李子川²,周志立³

1.江南大学数字科技与创意设计学院, 江苏无锡 214122;2.中国刑事警察学院公安信息技术与情报学院, 辽宁沈阳 110854;3.广州大学人工智能学院, 广东广州 511363

发布日期:2026-03-18
通讯作者: 曹恩国(1983— ),男,教授,博士,研究方向为人工智能辅助设计. E-mail:enguocao@jiangnan.edu.cn
作者简介:杨滨(1979— ),男,副教授,博士,研究方向为数字图像处理,人工智能辅助设计. E-mail:yangbin@jiangnan.edu.cn*通信作者:曹恩国(1983— ),男,教授,博士,研究方向为人工智能辅助设计. E-mail:enguocao@jiangnan.edu.cn
基金资助:
国家自然科学基金资助项目(21BG131);江苏高校哲学社会科学研究重大资助项目(2025SJZD110);辽宁网络安全执法协同创新中心资助项目

Forensic analysis of poster design infringement based on visual salient features

YANG Bin¹, SUN Jiannan¹, CAO Enguo^1*, LI Zichuan², ZHOU Zhili³

1. School of Digital Technology&
Innovation Design, Jiangnan University, Wuxi 214122, Jiangsu, China;
2. School of Public Security Information Technology and Information, Criminal Investigation Police University of China, Shenyang 110854, Liaoning, China;
3. Institute of Artificial Intelligence, Guangzhou University, Guangzhou 511363, Guangdong, China

Published:2026-03-18

摘要/Abstract

摘要： 为辅助专家进行概念侵权行为的检测与判定,本文提出一种基于视觉显著性特征的海报设计侵权行为取证方法。提出设计了一个包含4个子网络的复杂深度学习模型,用于处理设计作品中的复杂视觉元素,并明确地划分出主要的版式结构关系。通过计算海报与现有作品之间的相似度,本方法能有效检测出设计师的侵权行为。实验结果显示,本方法在海报设计侵权行为取证分析上的准确率较传统方法有显著提升。

关键词: 图像处理, 相似度计算, 视觉显著性, 抄袭检测, 侵权行为取证

Abstract: Traditional clone detection methods primarily rely on pixel-level image similarities, often overlooking conceptual similarities in core design elements, particularly in compositional layouts. To address this limitation, we propose a forensic method for detecting poster design infringement based on visual saliency features, aimed at assisting experts in identifying and assessing conceptual plagiarism. To achieve this goal, a sophisticated deep learning model comprising four sub-networks is developed to process complex visual elements in design works and explicitly delineate key layout structural relationships. By computing conceptual feature similarities between posters and existing works, proposed method effectively identifies designers infringing behaviors. The experimental results demonstrate significant improvements in accuracy compared to traditional approaches in poster design infringement forensic analysis.

Key words: image processing, similarity calculation, visual saliency, plagiarism detection, infringement evidence collection

中图分类号:

TP309

杨滨,孙建楠,曹恩国,李子川,周志立. 基于显著性特征的海报设计侵权检测分析[J]. 《山东大学学报(理学版)》, 2026, 61(3): 11-19.

YANG Bin, SUN Jiannan, CAO Enguo, LI Zichuan, ZHOU Zhili. Forensic analysis of poster design infringement based on visual salient features[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(3): 11-19.

参考文献

[1] 余晓春. 美术作品实质性相似判断研究[D]. 武汉:中南财经政法大学,2020. YU Xiaochun. Study on substantial similarity determination of art works[D]. Wuhan: Zhongnan University of Economics and Law, 2020.
[2] YANG B, SUN X, GUO H, et al. A copy-move forgery detection method based on CMFD-SIFT[J]. Multimedia Tools and Applications, 2018, 77(1):837-855.
[3] RATHORE N K, JAIN N K, SHUKLA P K, et al. Image forgery detection using singular value decomposition with some attacks[J]. National Academy Science Letters, 2021, 44(4):331-338.
[4] GOEL N, KAUR S, BALA R. Dual branch convolutional neural network for copy move forgery detection[J]. IET Image Processing, 2021, 15(3):656-665.
[5] LANG Y, HE Y, YANG F, et al. Which is plagiarism: fashion image retrieval based on regional representation for design protection[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020:12645-12654.
[6] CUI S, LIU F, ZHOU T, et al. Understanding and identifying artwork plagiarism with the wisdom of designers: a case study on poster artworks[C] //Proceedings of the 30th ACM International Conference on Multimedia. Lisbon: ACM, 2022:2043-2051.
[7] LIU Z H, YANG B, AN J R, et al. Similarity evaluation of graphic design based on deep visual saliency features[J]. Journal of Supercomputing, 2023, 79(9):21346-21367.
[8] BORJI A, ITTI L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1):185-207.
[9] TSOTSOS J K, CULHANE S M, KEI WAI W Y, et al. Modeling visual attention via selective tuning[J]. Artificial Intelligence, 1995, 78(1-2):507-545.
[10] ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11):1254-1259.
[11] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C] //2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009:1597-1604.
[12] HAN L, LI X, DONG Y. SalNet: edge constraint based end-to-end model for salient object detection[C] //Chinese Conference on Pattern Recognition and Computer Vision(PRCV). Guangzhou: Springer, 2018:417-428.
[13] LIU N, HAN J. DHSnet: Deep hierarchical saliency network for salient object detection[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016:678-686.
[14] HOU Q, CHENG M M, HU X, et al. Deeply supervised salient object detection with short connections[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4):815-828.
[15] LI J, SU J, XIA C, et al. Salient object detection with purificatory mechanism and structural similarity loss[J]. IEEE Transactions on Image Processing, 2021, 30:6855-6868.
[16] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C] //Proceedings of the European Conference on Computer Vision(ECCV). Munich: Springer, 2018:3-19.
[17] ODONOVAN P, AGARWALA A, HERTZMANN A. Learning layouts for single-pagegraphic designs[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(8):1200-1213.
[18] YANG B. Perceptual similarity measurement based on generative adversarial neural networks in graphics design[J]. Applied Soft Computing, 2021, 110:107548.
[19] 张舜尧,李华旺,张永合,等. 基于独立注意力机制的图像检索算法[J]. 计算机科学,2023,50(1):328-333. ZHANG Shunyao, LI Huawang, ZHANG Yonghe, et al. Image retrieval based on independent attention mechanism[J]. Computer Science, 2023, 50(1):328-333.

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基于显著性特征的海报设计侵权检测分析

Forensic analysis of poster design infringement based on visual salient features

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