Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches.
Fraud early detection: The misleading or fake information spread by malicious users could lead to catastrophic consequences because the openness of online social media enables the information to be spread in a timely manner. Therefore, detecting fake information or malicious users is a critical research topic @cite_13 @cite_3 @cite_4 @cite_0 @cite_1 . In recent years, extensive studies focus on the rumor early detection @cite_19 @cite_22 . Besides early detecting the fake information, early detecting the malicious users who create the fake information is also important. @cite_20 @cite_11 aim to early detect vandals in Wikipedia. All the existing approaches adopt classification models for fraud early detection. In this work, we combine the survival analysis with RNN to predict whether a user is a fraudster.
@cite_4 @cite_22 @cite_1 @cite_3 @cite_0 @cite_19 @cite_13 @cite_20 @cite_11
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False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research. In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound impact than ever before. This makes detection of fake news an extremely important challenge. Fake news articles, just like genuine news articles, leverage multimedia content to manipulate user opinions but spread misinformation. A shortcoming of the current approaches for the detection of fake news is their inability to learn a shared representation of multimodal (textual + visual) information. We propose an end-to-end network, Multimodal Variational Autoencoder (MVAE), which uses a bimodal variational autoencoder coupled with a binary classifier for the task of fake news detection. The model consists of three main components, an encoder, a decoder and a fake news detector module. The variational autoencoder is capable of learning probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. The fake news detector then utilizes the multimodal representations obtained from the bimodal variational autoencoder to classify posts as fake or not. We conduct extensive experiments on two standard fake news datasets collected from popular microblogging websites: Weibo and Twitter. The experimental results show that across the two datasets, on average our model outperforms state-of-the-art methods by margins as large as 6 in accuracy and 5 in F1 scores. The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. At best, this leads to a loss of trust in digital content, but it might even cause further harm by spreading false information and the creation of fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them - either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of about 1.5 million manipulated images. Thus, this database is at least an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression and clearly outperforms human observers. While online social networks provide access to a massive information source, they also enable wide dissemination of false or inaccurate content. Undesirable results caused by misinformation propagation make its timely detection very imperative. An important question is how many monitors are required to detect all misinformation cascades at their early stage. To answer this question, we define a Time Constrained Misinformation Detection (TCMD) problem. As we have proved, there is no polynomial time (1 - e) ln n-approximation for the TCMD problem. The large number of independent misinformation cascades and heterogeneous delays make misinformation detection more challenging. Our approach includes stochastic programming and an O(ln(1 + n)) approximation algorithm for one-hop detection. This approach can provide a lower bound on the number of required monitors for general detection. Furthermore, we propose a network-compression based solution, whose effectiveness is validated by extensive experimental results.