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Perceptual similarity measurement based on generative adversarial neural networks in graphics design
Applied Soft Computing ( IF 5.472 ) Pub Date : 2021-06-10 , DOI: 10.1016/j.asoc.2021.107548
Bin Yang

Measuring the similarity between images is of paramount importance in computer vision. However, the commonly used pixelwise similarity metrics do not match well with perceptual similarity. The purpose of this paper is to propose a visual similarity measurement method, which can be effectively used for plagiarism detection in graphic design. Plagiarism detection of designs refers to the identification and determination of major similarities. It is difficult to carry out the similarity learning process in traditional deep neural network due to the insufficient of training samples. To overcome this problem, a novel scheme is proposed for measuring perceptual similarity of graphics by using a constraint Generative Adversarial Network (GAN) model. The generator of GAN is used to create similar graphics following the common plagiarism features of logo design. Unlike the traditional discriminator which judges the authenticity of the generated image and the original image, the modified discriminator is used to calculate the perceptual similarity of the graphics pair. In graphics design, plagiarism mainly focuses on the changes of shape, color and style, which has certain cognitive subjectivity. Therefore, design experts were invited to participate in a group of cognitive analysis experiments. A perceptual constraint model is established to limit the generation of plagiarized graphics according to “design and visual rationality”. Promising results demonstrate that the proposed method can be used for plagiarism detection of logo design. Given its effectiveness and conceptual simplicity, I hope it can serve as a baseline and contribute to the future research on plagiarism detection of artworks.



中文翻译:

图形设计中基于生成对抗神经网络的感知相似度测量

测量图像之间的相似性在计算机视觉中至关重要。然而,常用的逐像素相似性度量与感知相似性不匹配。本文的目的是提出一种视觉相似度测量方法,可以有效地用于平面设计中的抄袭检测。设计抄袭检测是指识别和确定主要相似之处。由于训练样本不足,在传统的深度神经网络中很难进行相似性学习过程。为了克服这个问题,提出了一种通过使用约束生成对抗网络(GAN)模型来测量图形的感知相似性的新方案。GAN 的生成器用于按照标志设计的常见抄袭特征创建类似的图形。与传统判别器判断生成图像和原始图像的真实性不同,修改后的判别器用于计算图形对的感知相似度。在平面设计中,抄袭主要集中在形状、颜色和风格的变化上,具有一定的认知主观性。因此,邀请了设计专家参与了一组认知分析实验。建立感知约束模型,根据“设计和视觉合理性”限制抄袭图形的产生。有希望的结果表明,所提出的方法可用于标志设计的抄袭检测。鉴于其有效性和概念上的简单性,

更新日期:2021-06-10
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