用动态系统建模生成对抗样本，提升攻击成功率与视觉相似度。
NODE-AdvGAN: Improving the transferability and perceptual similarity of adversarial examples by dynamic-system-driven adversarial generative model
- 将对抗样本生成视为连续过程，用神经微分方程模拟迭代优化。
- 在多个数据集上攻击成功率更高，且生成图像更接近原图。
- 适合研究模型鲁棒性、对抗攻击与生成模型的读者。
理解对抗样本对提升模型鲁棒性至关重要，因其能引入难以察觉的扰动以欺骗模型。有效的对抗样本可用来训练更鲁棒的模型，消除模型弱点。本文提出NODE-AdvGAN，将对抗样本生成视为连续过程，采用神经微分方程（NODE）模拟生成器动态。通过模仿传统基于梯度方法的迭代特性，该方法生成更平滑、更精确的扰动，添加到正常图像后仍保持高感知相似性。此外，提出新型训练策略NODE-AdvGAN-T，通过训练中调节噪声参数增强黑盒攻击的迁移能力。实验表明，与基线模型相比，NODE-AdvGAN和NODE-AdvGAN-T生成的对抗样本具有更高的攻击成功率，同时保留更优的感知质量。
原文摘要 · Abstract (English)
Understanding adversarial examples is crucial for improving model robustness, as they introduce imperceptible perturbations to deceive models. Effective adversarial examples, therefore, offer the potential to train more robust models by eliminating model singularities. We propose NODE-AdvGAN, a novel approach that treats adversarial generation as a continuous process and employs a Neural Ordinary Differential Equation (NODE) to simulate generator dynamics. By mimicking the iterative nature of traditional gradient-based methods, NODE-AdvGAN generates smoother and more precise perturbations that preserve high perceptual similarity when added to benign images. We also propose a new training strategy, NODE-AdvGAN-T, which enhances transferability in black-box attacks by tuning the noise parameters during training. Experiments demonstrate that NODE-AdvGAN and NODE-AdvGAN-T generate more effective adversarial examples that achieve higher attack success rates while preserving better perceptual quality than baseline models.
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