We present a generic Reinforcement Learning (RL) framework optimized for
crafting adversarial attacks on different model types spanning from ECG signal
analysis (1D), image classification (2D), and video classification (3D). The
framework focuses on identifying sensitive regions and inducing
misclassifications with minimal distortions and various distortion types. The
novel RL method outperforms state-of-the-art methods for all three
applications, proving its efficiency. Our RL approach produces superior
localization masks, enhancing interpretability for image classification and ECG
analysis models. For applications such as ECG analysis, our platform highlights
critical ECG segments for clinicians while ensuring resilience against
prevalent distortions. This comprehensive tool aims to bolster both resilience
with adversarial training and transparency across varied applications and data
types.

我们提出了一种针对不同模型类型的通用强化学习（RL）框架，用于制定对抗性攻击，涵盖了从 ECG 信号分析（1D）、图像分类（2D）到视频分类（3D）的多个领域。该框架专注于识别敏感区域，并在最小失真和各种失真类型的情况下引发错误分类。这种新型 RL 方法在所有三个应用中表现优于最先进的方法，证明了其有效性。我们的 RL 方法产生出优秀的定位掩膜，增强了图像分类和 ECG 分析模型的可解释性。对于 ECG 分析等应用，我们的平台突出显示医生关注的关键心电图片段，并保证抵御普遍的失真。这个综合工具旨在通过对抗性训练增强鲁棒性，并在各种应用和数据类型中提升透明度。

强化学习下的黑盒图像、视频和 ECG 信号分类的鲁棒性与可视化解释

Robustness and Visual Explanation for Black Box Image, Video, and ECG  Signal Classification with Reinforcement Learning

Although face recognition has made impressive progress in recent years, we
ignore the racial bias of the recognition system when we pursue a high level of
accuracy. Previous work found that for different races, face recognition
networks focus on different facial regions, and the sensitive regions of
darker-skinned people are much smaller. Based on this discovery, we propose a
new de-bias method based on gradient attention, called Gradient Attention
Balance Network (GABN). Specifically, we use the gradient attention map (GAM)
of the face recognition network to track the sensitive facial regions and make
the GAMs of different races tend to be consistent through adversarial learning.
This method mitigates the bias by making the network focus on similar facial
regions. In addition, we also use masks to erase the Top-N sensitive facial
regions, forcing the network to allocate its attention to a larger facial
region. This method expands the sensitive region of darker-skinned people and
further reduces the gap between GAM of darker-skinned people and GAM of
Caucasians. Extensive experiments show that GABN successfully mitigates racial
bias in face recognition and learns more balanced performance for people of
different races.

提出了一种新的基于梯度注意力的去偏置方法，称为 Gradient Attention Balance Network (GABN)，通过将面部识别网络的梯度注意力图用于跟踪敏感的面部区域，并通过对抗性学习使不同种族的梯度注意力图趋于一致，该方法通过使网络专注于相似的面部区域来缓解偏见。同时，该方法还使用口罩来擦除前 N 个敏感面部区域，迫使网络将其注意力分配到更大的面部区域，扩大了皮肤较暗的人的敏感区域，进一步减少了皮肤较暗的人的梯度注意力图和高加索人的梯度注意力图之间的差距。广泛的实验证明，GABN 成功地减轻了面部识别中的种族偏见，并为不同种族的人学习到更平衡的表现。