4 research outputs found
Deception through Half-Truths
Deception is a fundamental issue across a diverse array of settings, from
cybersecurity, where decoys (e.g., honeypots) are an important tool, to
politics that can feature politically motivated "leaks" and fake news about
candidates.Typical considerations of deception view it as providing false
information.However, just as important but less frequently studied is a more
tacit form where information is strategically hidden or leaked.We consider the
problem of how much an adversary can affect a principal's decision by
"half-truths", that is, by masking or hiding bits of information, when the
principal is oblivious to the presence of the adversary. The principal's
problem can be modeled as one of predicting future states of variables in a
dynamic Bayes network, and we show that, while theoretically the principal's
decisions can be made arbitrarily bad, the optimal attack is NP-hard to
approximate, even under strong assumptions favoring the attacker. However, we
also describe an important special case where the dependency of future states
on past states is additive, in which we can efficiently compute an
approximately optimal attack. Moreover, in networks with a linear transition
function we can solve the problem optimally in polynomial time
"Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing
The prevalence of half-truths, which are statements containing some truth but
that are ultimately deceptive, has risen with the increasing use of the
internet. To help combat this problem, we have created a comprehensive pipeline
consisting of a half-truth detection model and a claim editing model. Our
approach utilizes the T5 model for controlled claim editing; "controlled" here
means precise adjustments to select parts of a claim. Our methodology achieves
an average BLEU score of 0.88 (on a scale of 0-1) and a disinfo-debunk score of
85% on edited claims. Significantly, our T5-based approach outperforms other
Language Models such as GPT2, RoBERTa, PEGASUS, and Tailor, with average
improvements of 82%, 57%, 42%, and 23% in disinfo-debunk scores, respectively.
By extending the LIAR PLUS dataset, we achieve an F1 score of 82% for the
half-truth detection model, setting a new benchmark in the field. While
previous attempts have been made at half-truth detection, our approach is, to
the best of our knowledge, the first to attempt to debunk half-truths