From 715638fe373ccee2f84c8d7b173db15c53dc18f4 Mon Sep 17 00:00:00 2001
From: Neea Rusch <2580981+nkrusch@users.noreply.github.com>
Date: Fri, 16 Feb 2024 23:57:48 -0500
Subject: [PATCH] Update README.md

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 README.md | 6 +++---
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 # Constraint guaranteed evasion attacks
 
-<h3>We present an approach to introduce constraints to unconstrained adversarial machine learning evasion attacks.
-The technique is founded on a constraint validation algorithm, Contraint Guaranteed Evasion (CGE), that guarantees generated adversarial examples also satisfy domain constraints.</h3>
+<h4>We present an approach to introduce constraints to unconstrained adversarial machine learning evasion attacks.
+The technique is founded on a constraint validation algorithm, Contraint Guaranteed Evasion (CGE), that guarantees generated adversarial examples also satisfy domain constraints.</h4>
 
-<br/>This repository includes the full CGE implemenation, and an experimental setup for running various adversarial evasion attacks, enhanced with CGE, on different data sets and victim classifiers.
+This repository includes the full CGE implemenation, and an experimental setup for running various adversarial evasion attacks, enhanced with CGE, on different data sets and victim classifiers.
 The following experimental options are included.
 
 - **Attacks**: Projected Gradient Descent (PGD), Zeroth-Order Optimization (ZOO), HopSkipJump attack.