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A Word-Level Adversarial Attack Method Based on Sememes and an Improved Quantum-Behaved Particle Swarm Optimization.

Qidong Chen, Jun Sun, Vasile Palade

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary

    This study introduces a novel word-level adversarial attack using sememes and an improved quantum-behaved particle swarm optimization (QPSO) algorithm. The method generates effective adversarial examples for natural language processing models with high attack success and semantic similarity.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Computational Linguistics

    Background:

    • Textual adversarial attacks aim to manipulate model behavior by altering input text.
    • Existing methods face challenges in maintaining text quality and attack effectiveness.

    Purpose of the Study:

    • To propose an effective word-level adversarial attack method using sememes and an improved quantum-behaved particle swarm optimization (QPSO) algorithm.
    • To enhance the search for adversarial examples by reducing the search space and improving the QPSO algorithm's convergence and exploration capabilities.

    Main Methods:

    • Utilized a sememe-based word substitution strategy to create a reduced search space for adversarial examples.
    • Developed a historical information-guided QPSO with random drift local attractor (HIQPSO-RD) algorithm for efficient search.
    • Implemented a two-stage diversity control strategy to further optimize search performance.

    Main Results:

    • The proposed HIQPSO-RD method achieved higher attack success rates compared to state-of-the-art adversarial attack methods.
    • Generated adversarial examples exhibited lower modification rates while preserving semantic similarity and grammatical correctness.
    • Human evaluations confirmed the superior quality of adversarial examples in terms of grammaticality and perplexity (PPL).

    Conclusions:

    • The novel sememe-based and HIQPSO-RD approach offers a more effective and robust method for textual adversarial attacks.
    • This technique successfully balances attack efficacy with the preservation of linguistic properties in natural language processing models.