443 research outputs found
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Malicious crowdsourcing forums are gaining traction as sources of spreading
misinformation online, but are limited by the costs of hiring and managing
human workers. In this paper, we identify a new class of attacks that leverage
deep learning language models (Recurrent Neural Networks or RNNs) to automate
the generation of fake online reviews for products and services. Not only are
these attacks cheap and therefore more scalable, but they can control rate of
content output to eliminate the signature burstiness that makes crowdsourced
campaigns easy to detect.
Using Yelp reviews as an example platform, we show how a two phased review
generation and customization attack can produce reviews that are
indistinguishable by state-of-the-art statistical detectors. We conduct a
survey-based user study to show these reviews not only evade human detection,
but also score high on "usefulness" metrics by users. Finally, we develop novel
automated defenses against these attacks, by leveraging the lossy
transformation introduced by the RNN training and generation cycle. We consider
countermeasures against our mechanisms, show that they produce unattractive
cost-benefit tradeoffs for attackers, and that they can be further curtailed by
simple constraints imposed by online service providers
Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks
This paper investigates the potential of semi-supervised Generative
Adversarial Networks (GANs) to fine-tune pretrained language models in order to
classify Bengali fake reviews from real reviews with a few annotated data. With
the rise of social media and e-commerce, the ability to detect fake or
deceptive reviews is becoming increasingly important in order to protect
consumers from being misled by false information. Any machine learning model
will have trouble identifying a fake review, especially for a low resource
language like Bengali. We have demonstrated that the proposed semi-supervised
GAN-LM architecture (generative adversarial network on top of a pretrained
language model) is a viable solution in classifying Bengali fake reviews as the
experimental results suggest that even with only 1024 annotated samples,
BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and
a f1-score of 84.89% outperforming other pretrained language models -
BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and
10% respectively in terms of accuracy. The experiments were conducted on a
manually labeled food review dataset consisting of total 6014 real and fake
reviews collected from various social media groups. Researchers that are
experiencing difficulty recognizing not just fake reviews but other
classification issues owing to a lack of labeled data may find a solution in
our proposed methodology
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
We introduce an adversarial method for producing high-recall explanations of
neural text classifier decisions. Building on an existing architecture for
extractive explanations via hard attention, we add an adversarial layer which
scans the residual of the attention for remaining predictive signal. Motivated
by the important domain of detecting personal attacks in social media comments,
we additionally demonstrate the importance of manually setting a semantically
appropriate `default' behavior for the model by explicitly manipulating its
bias term. We develop a validation set of human-annotated personal attacks to
evaluate the impact of these changes.Comment: Accepted to EMNLP 2018 Code and data available at
https://github.com/shcarton/rcn
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
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