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The connectivity and openness of the Internet have cultivated a blistering expansion of online media websites. However, the culture of openness also makes the emerging platforms an effective channel for content pollution, such as fraud, phishing, and other online abuses. To complicate the problem, content polluters actively manipulate the characteristics of the Internet through establishing links with normal users and blending the malicious information with legitimate content. The manipulated links and content, being used as camouflage, make it very intricate to detect content polluters. Recent work has investigated camouflaged fraud in networks. However, due to the lack of availability of label information for camouflaged content, it is challenging to detect content polluters with traditional approaches. In this paper, we make the first attempt on detecting camouflaged content polluters. In order to evaluate the proposed approach, we conduct experiments on real-world data. The results show that our method achieves better results than existing approaches.