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Related Experiment Video

Updated: Nov 26, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
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Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

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SMGEA: A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories.

Zhaohui Che, Ali Borji, Guangtao Zhai

    IEEE Transactions on Neural Networks and Learning Systems
    |December 9, 2020
    PubMed
    Summary

    This study introduces a novel serial-minigroup-ensemble-attack (SMGEA) to improve adversarial example transferability in black-box scenarios. SMGEA enhances attack effectiveness against computer vision systems by preserving gradient information across model groups.

    Related Experiment Videos

    Last Updated: Nov 26, 2025

    Gradient Echo Quantum Memory in Warm Atomic Vapor
    10:00

    Gradient Echo Quantum Memory in Warm Atomic Vapor

    Published on: November 11, 2013

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    Area of Science:

    • Computer Vision
    • Machine Learning Security
    • Deep Neural Networks

    Background:

    • Deep neural networks are susceptible to adversarial attacks, posing security risks to black-box applications.
    • Existing transfer-based attacks have limited transferability due to small source model ensembles.
    • Query-based black-box attacks incur high costs and risk detection.

    Purpose of the Study:

    • To propose a novel transfer-based black-box attack, Serial-Minigroup-Ensemble-Attack (SMGEA).
    • To enhance the transferability and effectiveness of adversarial examples against target models.
    • To reduce the query cost and detection risk associated with black-box attacks.

    Main Methods:

    • Dividing source models into minigroups with novel ensemble strategies for intragroup transferability.
    • Employing a recursive algorithm to accumulate gradient memories across minigroups for intergroup transferability.
    • Evaluating SMGEA on various datasets and real-world online saliency prediction systems.

    Main Results:

    • SMGEA achieves state-of-the-art black-box attack performance across multiple datasets.
    • The attack successfully deceives two online saliency prediction systems: DeepGaze-II and SALICON.
    • Demonstrated improved intragroup and intergroup transferability compared to existing methods.

    Conclusions:

    • SMGEA offers a potent and efficient method for crafting transferable adversarial examples in black-box settings.
    • The proposed approach advances the security research of ubiquitous pixel-to-pixel computer vision tasks.
    • A new code repository and model zoo are released to facilitate further research in adversarial attacks and defenses.