The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this paper, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are quickly becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This article presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the article concerns how the results of the individual detectors are fused together into an overall decision classifying a user as misbehaving or not, based on Dempster-Shafer theory. The article introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com. SafeVchat has been deployed in Chatroulette. A combination of SafeVchat with human moderation has resulted in banning as many as 50,000 inappropriate users per day on Chatoulette. Furthermore, offensive content on Chatoulette has dropped significantly from 33.08% (before SafeVchat installation) to 3.49% (after SafeVchat installation).

The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.

Online video chat services such as Chatroulette [1] and Omegle [2] that randomly match pairs of users in video chat sessions have become increasingly popular, with over twenty thousand online users at anytime during a day. A key problem encountered in such systems is the presence of misbehaving users ("flashers") and obscene content. Our previous works [3] [4] prove that using some image recognition methods (skin-detection, dense SIFT) and machine learning algorithms could achieve significantly higher recall and better precision for flasher detection. Nowadays, with the rapid development of advanced mobile phones with both front and back cameras, we expect mobile video chat to become a popular extension of online video chat services. However, because of the computation-intensive features used by our previous solutions and mobile phones' hardware limitations such as memory size and CPU capacity, it is difficult to directly apply our previous works to mobile platforms. As smartphones are increasingly equipped with diverse sensing capabilities, we plan to utilize this multi-dimensional sensor information to extend flasher detection on mobile platform. This project explores how we can mine accelerometer and other mobile sensor data to infer some clues to optimize flasher detection accuracy while reducing the computation demands of flasher detection on the mobile device.

Online video chat services, such as Chatroulette, Omegle, and vChatter are becoming increasingly popular and have attracted millions of users. One critical problem encountered in such applications is the presence of misbehaving users ("flashers") and obscene content. Automatically filtering out obscene content from these systems in an efficient manner poses a difficult challenge. This paper presents a novel Fine-Grained Cascaded (FGC) classification solution that significantly speeds up the compute-intensive process of classifying misbehaving users by dividing image feature extraction into multiple stages and filtering out easily classified images in earlier stages, thus saving unnecessary computation costs of feature extraction in later stages. Our work is further enhanced by integrating new webcam-related contextual information (illumination and color) into the classification process, and a 2-stage soft margin SVM algorithm for combining multiple features. Evaluation results using real-world data set obtained from Chatroulette show that the proposed FGC based classification solution significantly outperforms state-of-the-art techniques.

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This paper presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not, based on Dempster-Shafer Theory. The paper introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com.