mlsanityData to 14 Oct 2026

A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate

arXiv 2105.00278 ↗First posted 1 May 2021cs.CV cs.CR cs.LGcited by 0

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Abstract (arXiv)

Most of the adversarial attack methods suffer from large perceptual distortions such as visible artifacts, when the attack strength is relatively high. These perceptual distortions contain a certain portion which contributes less to the attack success rate. This portion of distortions, which is induced by unnecessary modifications and lack of proper perceptual distortion constraint, is the target of the proposed framework. In this paper, we propose a perceptual distortion reduction framework to tackle this problem from two perspectives. Firstly, we propose a perceptual distortion constraint and add it into the objective function to jointly optimize the perceptual distortions and attack success rate. Secondly, we propose an adaptive penalty factor λ to balance the discrepancies between different samples. Since SGD and Momentum-SGD cannot optimize our complex non-convex problem, we exploit Adam in optimization. Extensive experiments have verified the superiority of our proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 67 references that are in the map. First posted on arXiv 1 May 2021.

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35 of 67 references.

References · 35

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23 more
  1. 2016Adversarial examples in the physical worldcited by 1,769
  2. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,451
  3. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  4. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  5. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 501
  6. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 462
  7. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 442
  8. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 355
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  11. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 195
  12. 2019Enhancing Adversarial Example Transferability with an Intermediate Level Attackcited by 140
  13. 2018Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defensescited by 109
  14. 2019NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networkscited by 94
  15. 2020Patch-wise Attack for Fooling Deep Neural Networkcited by 78
  16. 2021Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraintscited by 50
  17. 2020GreedyFool: Distortion-Aware Sparse Adversarial Attackcited by 37
  18. 2020Yet Another Intermediate-Level Attackcited by 35
  19. 2018Distributionally Adversarial Attackcited by 26
  20. 2019Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domaincited by 14
  21. 2019Adversarial attacks hidden in plain sightcited by 4
  22. 2020Frequency-Tuned Universal Adversarial Attackscited by 3
  23. 2020Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classificationcited by 1

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