提出跨范式攻击方法，让人群计数模型同时失效且难以察觉。
Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting
- 设计多任务损失框架，统一攻击密度图与点回归模型。
- 平均使误差提升7倍，且在7个顶尖模型上成功迁移。
- 攻击隐蔽性强，适合研究模型安全与防御机制者参考。
当前人群计数与定位主要基于密度图和点回归两种范式。鉴于该领域在安全上的重要性，模型对对抗攻击的鲁棒性备受关注。已有研究证明了基于密度图模型间的对抗补丁可迁移性，但跨范式攻击（即同时影响密度图与点回归模型）尚未被探索。本文提出一种新型对抗框架，通过综合多任务损失优化，同时破坏两类架构。针对点回归模型，采用场景密度特异性高置信度logit抑制；针对密度图方法，则使用峰值目标密度图抑制。两者结合模型无关的感知约束，确保扰动有效且人眼难以察觉。大量实验表明，该攻击使平均绝对误差相比干净图像提升7倍，同时保持良好视觉质量，并在7个先进人群模型上实现0.55至1.69的迁移率。相较现有可迁移攻击策略，本方法在攻击效果与隐蔽性间取得更好平衡。源代码已公开于https://github.com/simurgh7/CrowdGen。
原文摘要 · Abstract (English)
State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness against adversarial attacks. Recent studies have demonstrated transferability across density-map-based approaches via adversarial patches, but cross-paradigm attacks (i.e., across both density map-based models and point regression-based models) remain unexplored. We introduce a novel adversarial framework that compromises both density map and point regression architectural paradigms through a comprehensive multi-task loss optimization. For point-regression models, we employ scene-density-specific high-confidence logit suppression; for density-map approaches, we use peak-targeted density map suppression. Both are combined with model-agnostic perceptual constraints to ensure that perturbations are effective and imperceptible to the human eye. Extensive experiments demonstrate the effectiveness of our attack, achieving on average a 7X increase in Mean Absolute Error compared to clean images while maintaining competitive visual quality, and successfully transferring across seven state-of-the-art crowd models with transfer ratios ranging from 0.55 to 1.69. Our approach strikes a balance between attack effectiveness and imperceptibility compared to state-of-the-art transferable attack strategies. The source code is available at https://github.com/simurgh7/CrowdGen
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