# Deception through Half-Truths

arXiv: https://arxiv.org/abs/1911.05885 · HTML: https://mlsanity.com/paper/1911.05885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?](https://mlsanity.com/paper/2503.21717/) (2025)
- [The Missing Parts: Augmenting Fact Verification with Half-Truth Detection](https://mlsanity.com/paper/2508.00489/) (2025)
- [Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval](https://mlsanity.com/paper/2508.19758/) (2025)
- ["Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing](https://mlsanity.com/paper/2308.07973/) (2023)
- [Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection](https://mlsanity.com/paper/2604.19005/) (2026)
- [Learning Generative Deception Strategies in Combinatorial Masking Games](https://mlsanity.com/paper/2109.11637/) (2021)

## Abstract (arXiv)

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.