A Multi-Scale Isolation Forest Approach for Real-Time Detection and Filtering of FGSM Adversarial Attacks in Video Streams of Autonomous Vehicles

TL;DR

This paper introduces a multi-scale isolation forest method for real-time detection and filtering of FGSM adversarial attacks in video streams of autonomous vehicles, enhancing security in DNN-based image processing.

Contribution

The paper proposes a novel multi-scale isolation forest approach specifically designed for real-time detection of FGSM adversarial attacks in video streams.

Findings

Effectively filters adversarially perturbed images

Evaluated on 10,000 images with various perturbation levels

Mitigates impact of FGSM attacks in real-time

Abstract

Deep Neural Networks (DNNs) have demonstrated remarkable success across a wide range of tasks, particularly in fields such as image classification. However, DNNs are highly susceptible to adversarial attacks, where subtle perturbations are introduced to input images, leading to erroneous model outputs. In today's digital era, ensuring the security and integrity of images processed by DNNs is of critical importance. One of the most prominent adversarial attack methods is the Fast Gradient Sign Method (FGSM), which perturbs images in the direction of the loss gradient to deceive the model. This paper presents a novel approach for detecting and filtering FGSM adversarial attacks in image processing tasks. Our proposed method evaluates 10,000 images, each subjected to five different levels of perturbation, characterized by $\epsilon$ values of 0.01, 0.02, 0.05, 0.1, and 0.2. These perturbations are applied in the direction of the loss gradient. We demonstrate that our approach effectively filters adversarially perturbed images, mitigating the impact of FGSM attacks. The method is implemented in Python, and the source code is publicly available on GitHub for reproducibility and further research.