A supervised learning solution to detect Spam albums instead of spammers in Photo Spam was proposed. Spe-cifically, the characteristics of Photo Spam and the differences between Photo Spam and traditional Spam were analyzed. Then 12 features which were extracted easily and calculated efficiently were constructed based on the analysis. Next a classification model was built with a dataset of 2 356 labeled albums to identify Spam albums. The model provided ex-cellent performance with true positive rates of Spam albums and normal albums, reaching 100%and 98.2%respectively. Finally, the detection model were applied to 315 115 unlabeled albums and detected 89 163 spam albums with a true posi-tive rate of 97.2%.%提出一种针对Spam相册的检测方案。首先分析了Photo Spam的攻击特点以及与传统Spam的差异，在此基础上构造了12个提取及时且计算高效的特征。利用这些特征提出了有监督学习的检测模型，通过2356个相册的训练形成Spam相册分类器，实验表明能够正确检测到测试集中100%的Spam相册和98.2%的正常相册。最后将训练后的模型应用到包含315115个相册的真实数据集中，检测到89163个Spam相册，正确率达到97.2%。
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