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A Chaos Driven Metric for Backdoor Attack Detection

Hema Karnam Surendrababu, Nithin Nagaraj

TL;DR

In this defense technique, an integrated approach that combines chaos theory with manifold learning is proposed, and a novel metric - Precision Matrix Dependency Score (PDS) that is based on the conditional variance of Neurochaos features is formulated.

Abstract

The advancement and adoption of Artificial Intelligence (AI) models across diverse domains have transformed the way we interact with technology. However, it is essential to recognize that while AI models have introduced remarkable advancements, they also present inherent challenges such as their vulnerability to adversarial attacks. The current work proposes a novel defense mechanism against one of the most significant attack vectors of AI models - the backdoor attack via data poisoning of training datasets. In this defense technique, an integrated approach that combines chaos theory with manifold learning is proposed. A novel metric - Precision Matrix Dependency Score (PDS) that is based on the conditional variance of Neurochaos features is formulated. The PDS metric has been successfully evaluated to distinguish poisoned samples from non-poisoned samples across diverse datasets.

A Chaos Driven Metric for Backdoor Attack Detection

TL;DR

In this defense technique, an integrated approach that combines chaos theory with manifold learning is proposed, and a novel metric - Precision Matrix Dependency Score (PDS) that is based on the conditional variance of Neurochaos features is formulated.

Abstract

The advancement and adoption of Artificial Intelligence (AI) models across diverse domains have transformed the way we interact with technology. However, it is essential to recognize that while AI models have introduced remarkable advancements, they also present inherent challenges such as their vulnerability to adversarial attacks. The current work proposes a novel defense mechanism against one of the most significant attack vectors of AI models - the backdoor attack via data poisoning of training datasets. In this defense technique, an integrated approach that combines chaos theory with manifold learning is proposed. A novel metric - Precision Matrix Dependency Score (PDS) that is based on the conditional variance of Neurochaos features is formulated. The PDS metric has been successfully evaluated to distinguish poisoned samples from non-poisoned samples across diverse datasets.
Paper Structure (15 sections, 1 equation, 10 figures, 8 tables)

This paper contains 15 sections, 1 equation, 10 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Results
  5. Discussion
  6. Conclusions

Figures (10)

  • Figure 1: Backdoor Attack Experimental Setup.
  • Figure 2: Methodology for the Chaos based Precision Matrix Dependency Score for Backdoor Trigger Detection.
  • Figure 3: UMAP transformation on the Neurochaos features generated for the poisoned positive class samples of the SST-2 dataset with 5% poisoning ratio. NL Hyperparameters Initial Neural Activity = $0.930$, Discrimination threshold = $0.499$, $\epsilon$ = $0.05$.
  • Figure 4: UMAP transformation on the Neurochaos features generated for the poisoned positive class samples of the SST-2 dataset with 5% poisoning ratio. NL Hyperparameters Initial Neural Activity = $0.930$, Discrimination threshold = $0.499$, $\epsilon$ = $0.1$.
  • Figure 5: UMAP transformation on the Neurochaos features generated for the poisoned positive class samples of the SST-2 dataset with 5% poisoning ratio. NL Hyperparameters Initial Neural Activity = $0.930$, Discrimination threshold = $0.499$, $\epsilon$ = $0.3$.
  • ...and 5 more figures