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Documentation of Nursing Diagnosis

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Updated: Sep 5, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Guidance Through Surrogate: Toward a Generic Diagnostic Attack.

Muzammal Naseer, Salman Khan, Fatih Porikli

IEEE Transactions on Neural Networks and Learning Systems

|July 11, 2022

Summary

This summary is machine-generated.

Adversarial training (AT) can mask gradients, creating false robustness. A new guided projected gradient attack (G-PGA) reveals this hidden vulnerability by efficiently finding adversarial directions.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Adversarial training (AT) enhances deep neural network robustness against attacks.
Some defenses exhibit robustness due to gradient masking, hindering attack effectiveness.
Label smoothing in AT is a potential cause of gradient masking.

Purpose of the Study:

Analyze label smoothing's role in gradient masking during adversarial training.
Develop a novel attack to reveal hidden robustness caused by gradient masking.
Propose G-PGA as a diagnostic tool for adversarial defense evaluation.

Main Methods:

Investigated label smoothing's impact on adversarial training.
Developed a guided projected gradient attack (G-PGA) using a "match and deceive" loss.
Utilized a surrogate model to guide the attack and avoid local minima.
Integrated G-PGA with ensemble strategies like auto-attack for efficiency.

Main Results:

Demonstrated that label smoothing can contribute to gradient masking.
G-PGA effectively identifies elusive robustness without random restarts or excessive iterations.
G-PGA shows improved efficiency and convergence when combined with ensemble attacks.
G-PGA successfully reveals gradient masking in adversarial defenses.

Conclusions:

Label smoothing can inadvertently create gradient masking, leading to overestimated adversarial robustness.
G-PGA is an efficient and effective attack method for diagnosing gradient masking.
The proposed attack serves as a valuable tool for evaluating the true robustness of adversarial defenses.