Quality Criteria and Method of Synthesis for Adversarial Attack-Resistant Classifiers

Abstract

The actual problem of adversarial attacks on classifiers, mainly implemented using deep neural networks, is considered. This problem is analyzed with a generalization to the case of any classifiers synthesized by machine learning methods. The imperfection of generally accepted criteria for assessing the quality of classifiers, including those used to confirm the effectiveness of protection measures against adversarial attacks, is noted. The reason for the appearance of adversarial examples and other errors of classifiers based on machine learning is investigated. A method for modeling adversarial attacks with a demonstration of the main effects observed during the attack is proposed. It is noted that it is necessary to develop quality criteria for classifiers in terms of potential susceptibility to adversarial attacks. To assess resistance to adversarial attacks, it is proposed to use the multidimensional EDCAP criterion (Excess, Deficit, Coating, Approx, Pref). We also propose a method for synthesizing a new EnAE (Ensemble of Auto-Encoders) multiclass classifier based on an ensemble of quality-controlled one-class classifiers according to EDCAP criteria. The EnAE classification algorithm implements a hard voting approach and can detect anomalous inputs. The proposed criterion, synthesis method and classifier are tested on several data sets with a medium dimension of the feature space.

Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

“…The primary objective of the adversarial attacks is to take into account the model's vulnerabilities and craft adversarial input to fool the DNN model into producing incorrect results [5][6][7][8]. Various adversarial attack strategies have recently been proposed to fool the DNN model into producing incorrect results in different application domains [9][10][11][12][13][14].…”