Q-TART: Quickly Training for Adversarial Robustness and in-Transferability

Improving Adversarial Transferability with Scheduled Step Size and Dual Example

Deep neural networks are widely known to be vulnerable to adversarial examples, especially showing significantly poor performance on adversarial examples generated under the white-box setting. However, most white-box attack methods rely heavily on the target model and quickly get stuck in local optima, resulting in poor adversarial transferability. The momentum-based methods and their variants are proposed to escape the local optima for better transferability. In this work, we notice that the transferability of adversarial examples generated by the iterative fast gradient sign method (I-FGSM) …

Relevance Attack on Detectors

This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures. To pursue a high attack transferability, one plausible way is to find a common property across detectors, which facilitates the discovery of common weaknesses. We are the first to suggest that the relevance map from interpreters for detectors is such a property. Based on it, we design a Relevance Attack on Detectors (RAD), which achieves a state-of-the-art transferability, exceeding exi…

Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration

Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable targeted adversarial examples due to the issue of vanishing gradient. In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly ob…

Generalizable Black-Box Adversarial Attack with Meta Learning

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning…

自动驾驶汽车中驾驶员观察模型从仿真环境到真实环境的可迁移性研究

On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars

本文研究驾驶员观察模型从仿真到真实环境的迁移性，通过构建真实驾驶数据集并使用I3D模型与Grad-CAM分析，发现模型平均准确率从85.7%降至46.6%，揭示了迁移挑战并促进鲁棒系统研究。

PAR-AdvGAN：利用渐进自回归AdvGAN提升对抗攻击能力

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN

PAR-AdvGAN通过在渐进生成网络中引入自回归迭代机制，提升对抗样本的攻击能力与生成速度，在多项黑盒攻击实验中优于现有方法。