White-Box Adversarial Attack For Handcrafted Features

Abstract

Traditionally, adversarial attacks on handcrafted features have functioned within black box paradigms, separating the adversarial noise design from the nature of these features. This thesis presents a novel change by putting forth a white-box adversarial attack against handcrafted features like ORB, SIFT, SURF, and so forth. By integrating feature detection and descriptor formulation into the adversarial example generation process, this clever method enriches the adversarial environment. Although vulnerabilities in deep networks have been made public by adversarial examples, the shortcomings of handcrafted features in adversarial environments have gone unnoticed. Through structural-level analysis of these feature algorithms, this work introduces new adversarial perturbations specifically designed for handcrafted features, revealing subtle yet powerful changes. The performance of features such as ORB, Fast SIFT, and SURF, which show generalizability across different features, viewpoints, and lighting conditions, is severely compromised by these modifications. Our understanding of the difficulties presented by these features is improved by this research, which also advances white-box adversarial attacks on handcrafted features. Our adversarial attack, which explores the complexities of feature detection and descriptor formulation, is an intelligent and valuable investigation that opens up new avenues for the secure and reliable use of handcrafted features in computer vision and machine learning fields. i