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<p>Recent advances in meta-learning have been successfully introduced into black-box adversarial attacks, enabling the Meta Conditional Generator (MCG) to quickly adapt new tasks and generate effective perturbations. However, there are two key issues in the current application of meta-learning in black-box adversarial attacks: existing “demand-based” task importance allocation causes MCG overfitting and limited generalization (contrary to meta-learning’s goal of learning general cross-task meta-features) due to deep neural networks’ strong learning ability; random batch sampling leads to training instability and high data demand. To address these issues, this paper constructs the meta-balance-learning (MBL) framework and introduces it into black-box adversarial sample attacks. The core innovations are reflected in two aspects: the Meta Gradient Balance mechanism perceives task difficulty by evaluating task gradients, dynamically adjusts task weights to balance importance allocation, optimizes the quality of meta-features, alleviates MCG overfitting, and improves generalization ability; the Batch-level Complete Coverage data sampling scheme ensures that each batch of data covers all task categories, which not only solves the problem of training instability but also reduces the demand for training data. Experiments show MBL uses only 10% of compared methods’ training data while achieving comparable median query counts and attack success rates. On CIFAR-10 and MNIST, its average query counts drop by 9.1−16.8% and 17.8−46.9% across architectures, showing significant comprehensive advantages. The implementation is available at <a href="https://github.com/luotan369/MBL.">https://github.com/luotan369/MBL.</a></p>

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Task-aware meta-balance-learning for better generalizable black-box adversarial attack

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Tan Luo,
  • Yaochi Zhao,
  • Zhuhua Hu,
  • Jiezhuo Zhong,
  • Haoran Liu

摘要

Recent advances in meta-learning have been successfully introduced into black-box adversarial attacks, enabling the Meta Conditional Generator (MCG) to quickly adapt new tasks and generate effective perturbations. However, there are two key issues in the current application of meta-learning in black-box adversarial attacks: existing “demand-based” task importance allocation causes MCG overfitting and limited generalization (contrary to meta-learning’s goal of learning general cross-task meta-features) due to deep neural networks’ strong learning ability; random batch sampling leads to training instability and high data demand. To address these issues, this paper constructs the meta-balance-learning (MBL) framework and introduces it into black-box adversarial sample attacks. The core innovations are reflected in two aspects: the Meta Gradient Balance mechanism perceives task difficulty by evaluating task gradients, dynamically adjusts task weights to balance importance allocation, optimizes the quality of meta-features, alleviates MCG overfitting, and improves generalization ability; the Batch-level Complete Coverage data sampling scheme ensures that each batch of data covers all task categories, which not only solves the problem of training instability but also reduces the demand for training data. Experiments show MBL uses only 10% of compared methods’ training data while achieving comparable median query counts and attack success rates. On CIFAR-10 and MNIST, its average query counts drop by 9.1−16.8% and 17.8−46.9% across architectures, showing significant comprehensive advantages. The implementation is available at https://github.com/luotan369/MBL.