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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 80-92    DOI: 10.11925/infotech.2096-3467.2020.0046
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Microblog Image Privacy Classification with Deep Transfer Learning
Wang Shuyi(),Liu Sai,Ma Zheng
Management School, Tianjin Normal University, Tianjin 300387, China
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[Objective] This paper proposed a Social Network Image Privacy classifier based on transfer learning to provide reasonable hints for users to avoid accidentally uploading private information.[Methods] A new standard image dataset was created by gathering and annotating images from the Weibo platform. The deep transfer learning and fine-tuning of various image pre-training models were applied to classify whether the Weibo images contain privacy information or not automatically.[Results] With the same amount of data, the accuracy of transfer learning is improved by at least 30 percent compared to non-transfer learning approaches. Most ResNet deep neural network architectures can achieve more than 88% accuracy with transfer learning. Among them, ResNet50 has the highest recall rate (94.31%), accuracy (90.80%) and F1 value (91.11%), and the shortest testing time (148s). It has been selected out after comprehensive measurements of the above metrics and recommended as the most suitable model structure for current scenario requirements.[Limitations] The amount of labeled data in this study is relatively small, which may not be able to cover all the types of private information.[Conclusions] This study validates the feasibility and efficiency of deep transfer learning in the field of classification of private Weibo images. The result can be applied to various types of social media platforms to warn users about the risk of privacy leaking. The annotated image dataset can be used in others’ further researches as both a foundation and a comparison.

Key wordsPrivacy Protection      Machine Learning      Deep Transfer Learning     
Received: 10 January 2020      Published: 28 July 2020
ZTFLH:  G203  
Corresponding Authors: Wang Shuyi     E-mail:

Cite this article:

Wang Shuyi,Liu Sai,Ma Zheng. Microblog Image Privacy Classification with Deep Transfer Learning. Data Analysis and Knowledge Discovery, 2020, 4(10): 80-92.

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Illustration of Deep Transfer Learning
Flow Chart of the Experiment
隐私信息类别 二级隐私信息类别
个人基本信息 姓名、生日、出生地、性别、国籍
个人生活 民族、宗教、性取向、婚姻状况、酗酒、违法记录
生物识别信息 个人基因、面部特征、指纹、掌纹、耳廓、虹膜、
健康信息 医疗记录、病史、身体状况相关指标
证照信息 身份证、驾驶证、护照、居住证、社保卡、军官证、工作证、学生证、车辆牌照
财产信息 银行账号、转账支付记录(包括法定与虚拟货币)、房产信息、借贷信息、收据、票根
通讯信息 电话号码、电子邮箱地址、网络系统账号、IP地址、通讯录(包括本地和在线)、上网记录
位置信息 精准定位、住址、行踪轨迹、住宿信息、经纬度
教育/工作信息 学历、学位、教育经历、成绩单、职业、职位、工作单位、工作经历、培训记录、工作场合
关系信息 家庭关系、社交圈、职业圈、集会
List of Privacy Information Categories
Examples of Training Data
数据集 私密 公开 总计
训练集 685 1 134 1 819
验证集 241 358 599
测试集 299 299 598
Statistics of All Datasets
网络结构 训练时长/s 最优模型轮数 损失值 准确率
AlexNet 4 889 11 0.29 86.24%
ResNet18 4 899 14 0.28 89.05%
ResNet34 4 889 5 0.28 88.56%
ResNet50 4 910 8 0.26 90.38%
ResNet101 4 921 8 0.28 89.39%
ResNet152 5 288 5 0.27 90.05%
Models Training Results
Confusion Matrix of Classification Results
网络结构 测试时长/s 准确率 F1值 精准率 召回率
AlexNet 157 85.28% 85.85% 82.66% 89.30%
ResNet18 195 89.63% 89.74% 88.85% 90.64%
ResNet34 168 87.96% 88.57% 84.29% 93.31%
ResNet50 148 90.80% 91.11% 88.12% 94.31%
ResNet101 180 88.29% 88.78% 85.23% 92.64%
ResNet152 152 87.79% 88.24% 85.09% 91.64%
Testing Results of Transfer Learning Models
Random Examples of ResNet50 Model Testing Results
Wrong Predictions of Model ResNet50
网络结构 测试时长/s 准确率 F1值 精准率 召回率
AlexNet 149 52.17% 66.90% 51.15% 96.66%
ResNet18 152 53.18% 25.93% 62.03% 16.39%
ResNet34 146 53.18% 55.56% 52.87% 58.53%
ResNet50 155 53.68% 60.82% 52.70% 71.91%
ResNet101 159 55.18% 51.45% 56.13% 47.49%
ResNet152 166 51.67% 61.62% 51.10% 77.59%
Testing Results of Non-Transfer Learning Models
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