3,179 research outputs found
Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
Large-scale dissemination of disinformation online intended to mislead or
deceive the general population is a major societal problem. Rapid progression
in image, video, and natural language generative models has only exacerbated
this situation and intensified our need for an effective defense mechanism.
While existing approaches have been proposed to defend against neural fake
news, they are generally constrained to the very limited setting where articles
only have text and metadata such as the title and authors. In this paper, we
introduce the more realistic and challenging task of defending against
machine-generated news that also includes images and captions. To identify the
possible weaknesses that adversaries can exploit, we create a NeuralNews
dataset composed of 4 different types of generated articles as well as conduct
a series of human user study experiments based on this dataset. In addition to
the valuable insights gleaned from our user study experiments, we provide a
relatively effective approach based on detecting visual-semantic
inconsistencies, which will serve as an effective first line of defense and a
useful reference for future work in defending against machine-generated
disinformation.Comment: Accepted at EMNLP 202
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
Defending Elections Against Malicious Spread of Misinformation
The integrity of democratic elections depends on voters' access to accurate
information. However, modern media environments, which are dominated by social
media, provide malicious actors with unprecedented ability to manipulate
elections via misinformation, such as fake news. We study a zero-sum game
between an attacker, who attempts to subvert an election by propagating a fake
new story or other misinformation over a set of advertising channels, and a
defender who attempts to limit the attacker's impact. Computing an equilibrium
in this game is challenging as even the pure strategy sets of players are
exponential. Nevertheless, we give provable polynomial-time approximation
algorithms for computing the defender's minimax optimal strategy across a range
of settings, encompassing different population structures as well as models of
the information available to each player. Experimental results confirm that our
algorithms provide near-optimal defender strategies and showcase variations in
the difficulty of defending elections depending on the resources and knowledge
available to the defender.Comment: Full version of paper accepted to AAAI 201
Detecting cross-modal inconsistency to defend against neural fake news
Large-scale dissemination of disinformation online intended to mislead or deceive the general population is a major societal problem. Rapid progression in image, video, and natural language generative models has only exacerbated this situation and intensified our need for an effective defense mechanism. While existing approaches have been proposed to defend against neural fake news, they are generally constrained to the very limited setting where articles only have text and metadata such as the title and authors. In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset which is comprised of 4 different types of generated articles as well as conduct a series of human user study experiments based on this dataset. Coupled with providing a relatively effective approach based on detecting visual-semantic inconsistencies, the valuable insights gleaned from our user study experiments and, consequently, this paper will serve as an effective first line of defense and a valuable reference for future work in defending against machine-generated disinformation.Published versio
TextGAIL: Generative Adversarial Imitation Learning for Text Generation
Generative Adversarial Networks (GANs) for text generation have recently
received many criticisms, as they perform worse than their MLE counterparts. We
suspect previous text GANs' inferior performance is due to the lack of a
reliable guiding signal in their discriminators. To address this problem, we
propose a generative adversarial imitation learning framework for text
generation that uses large pre-trained language models to provide more reliable
reward guidance. Our approach uses contrastive discriminator, and proximal
policy optimization (PPO) to stabilize and improve text generation performance.
For evaluation, we conduct experiments on a diverse set of unconditional and
conditional text generation tasks. Experimental results show that TextGAIL
achieves better performance in terms of both quality and diversity than the MLE
baseline. We also validate our intuition that TextGAIL's discriminator
demonstrates the capability of providing reasonable rewards with an additional
task.Comment: AAAI 202
The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity
News articles covering policy issues are an essential source of information
in the social sciences and are also frequently used for other use cases, e.g.,
to train NLP language models. To derive meaningful insights from the analysis
of news, large datasets are required that represent real-world distributions,
e.g., with respect to the contained outlets' popularity, topically, or across
time. Information on the political leanings of media publishers is often
needed, e.g., to study differences in news reporting across the political
spectrum, which is one of the prime use cases in the social sciences when
studying media bias and related societal issues. Concerning these requirements,
existing datasets have major flaws, resulting in redundant and cumbersome
effort in the research community for dataset creation. To fill this gap, we
present POLUSA, a dataset that represents the online media landscape as
perceived by an average US news consumer. The dataset contains 0.9M articles
covering policy topics published between Jan. 2017 and Aug. 2019 by 18 news
outlets representing the political spectrum. Each outlet is labeled by its
political leaning, which we derive using a systematic aggregation of eight data
sources. The news dataset is balanced with respect to publication date and
outlet popularity. POLUSA enables studying a variety of subjects, e.g., media
effects and political partisanship. Due to its size, the dataset allows to
utilize data-intense deep learning methods.Comment: 2 pages, 1 tabl
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