# Semantics Preserving Adversarial Learning

arXiv: https://arxiv.org/abs/1903.03905 · HTML: https://mlsanity.com/paper/1903.03905/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Localized Uncertainty Attacks](https://mlsanity.com/paper/2106.09222/) (2021)
- [Accountable and Explainable Methods for Complex Reasoning over Text](https://mlsanity.com/paper/2211.04946/) (2022)

## Abstract (arXiv)

While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a framework to generate semantics preserving adversarial examples. First, we present a manifold learning method to capture the semantics of the inputs. The motivating principle is to learn the low-dimensional geometric summaries of the inputs via statistical inference. Then, we perturb the elements of the learned manifold using the Gram-Schmidt process to induce the perturbed elements to remain in the manifold. To produce adversarial examples, we propose an efficient algorithm whereby we leverage the semantics of the inputs as a source of knowledge upon which we impose adversarial constraints. We apply our approach on toy data, images and text, and show its effectiveness in producing semantics preserving adversarial examples which evade existing defenses against adversarial attacks.