Impact of Adversarial Attacks on Deep Learning Model Explainability

2024-12-15 · Gazi Nazia Nur, Mohammad Ahnaf Sadat

In this paper, we investigate the impact of adversarial attacks on the explainability of deep learning models, which are commonly criticized for their black-box nature despite their capacity for autonomous feature extraction. This black-box nature can affect the perceived trustworthiness of these models. To address this, explainability techniques such as GradCAM, SmoothGrad, and LIME have been developed to clarify model decision-making processes. Our research focuses on the robustness of these explanations when models are subjected to adversarial attacks, specifically those involving subtle image perturbations that are imperceptible to humans but can significantly mislead models. For this, we utilize attack methods like the Fast Gradient Sign Method (FGSM) and the Basic Iterative Method (BIM) and observe their effects on model accuracy and explanations. The results reveal a substantial decline in model accuracy, with accuracies dropping from 89.94% to 58.73% and 45.50% under FGSM and BIM attacks, respectively. Despite these declines in accuracy, the explanation of the models measured by metrics such as Intersection over Union (IoU) and Root Mean Square Error (RMSE) shows negligible changes, suggesting that these metrics may not be sensitive enough to detect the presence of adversarial perturbations.

📄 PDF Abstract BibTeX arXiv:2412.11119

Code (0)

등록된 구현이 없습니다.

Tasks

Decision MakingDeep Learning

Methods 이 논문이 사용한 방법론

LIME LIME, or Local Interpretable Model-Agnostic Explanations, is an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by…

Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation

2023-12-28 · Dibaloke Chanda, Saba Heidari Gheshlaghi, Nasim Yahya Soltani

Despite the success of graph neural networks (GNNs) in various domains, they exhibit susceptibility to adversarial attacks. Understanding these vulnerabilities is crucial for developing robust and secure applications. In…

Adversarial Attack Node Classification

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning

2023-07-17 · Prathyusha Devabhakthini, Sasmita Parida, Raj Mani Shukla, Suvendu Chandan Nayak

Adversarial attacks are a type of attack on machine learning models where an attacker deliberately modifies the inputs to cause the model to make incorrect predictions. Adversarial attacks can have serious consequences, …

Autonomous Vehicles Classification Medical Diagnosis text-classification+1

Algebraic Adversarial Attacks on Explainability Models

2025-03-16 · Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng 외

Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated…

Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability

2024-11-15 · Pooja Aslami, Kejun Chen, Timothy M. Hansen, Malik Hassanaly

False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also actively developed, we show that they remain…

Continual Learning Reinforcement Learning (RL)

Transferable Perturbations of Deep Feature Distributions

2020-04-27 · ICLR 2020 1 · Nathan Inkawhich, Kevin J Liang, Lawrence Carin, Yiran Chen

Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the modeling and exploitation of class-wise…

Adversarial Attack