Adversarial algorithms have shown to be effective against neural networks for
a variety of tasks. Some adversarial algorithms perturb all the pixels in the
image minimally for the image classification task in image classification. In
contrast, some algorithms perturb few pixels strongly. However, very little
information is available regarding why these adversarial samples so diverse
from each other exist. Recently, Vargas et al. showed that the existence of
these adversarial samples might be due to conflicting saliency within the
neural network. We test this hypothesis of conflicting saliency by analysing
the Saliency Maps (SM) and Gradient-weighted Class Activation Maps (Grad-CAM)
of original and few different types of adversarial samples. We also analyse how
different adversarial samples distort the attention of the neural network
compared to original samples. We show that in the case of Pixel Attack,
perturbed pixels either calls the network attention to themselves or divert the
attention from them. Simultaneously, the Projected Gradient Descent Attack
perturbs pixels so that intermediate layers inside the neural network lose
attention for the correct class. We also show that both attacks affect the
saliency map and activation maps differently. Thus, shedding light on why some
defences successful against some attacks remain vulnerable against other
attacks. We hope that this analysis will improve understanding of the existence
and the effect of adversarial samples and enable the community to develop more
robust neural networks.

通过分析 Saliency Maps 和 Gradient-weighted Class Activation Maps 的方式，研究 Adversarial algorithms 如何影响神经网络的关注点，防止了一些防御成功而留下另一些迎接攻击的漏洞。

深度神经网络对抗性图像注意力降低

Deep neural network loses attention to adversarial images

We study the multi-armed bandit (MAB) problem with composite and anonymous
feedback. In this model, the reward of pulling an arm spreads over a period of
time (we call this period as reward interval) and the player receives partial
rewards of the action, convoluted with rewards from pulling other arms,
successively. Existing results on this model require prior knowledge about the
reward interval size as an input to their algorithms. In this paper, we propose
adaptive algorithms for both the stochastic and the adversarial cases, without
requiring any prior information about the reward interval. For the stochastic
case, we prove that our algorithm guarantees a regret that matches the lower
bounds (in order). For the adversarial case, we propose the first algorithm to
jointly handle non-oblivious adversary and unknown reward interval size. We
also conduct simulations based on real-world dataset. The results show that our
algorithms outperform existing benchmarks.

本文研究使用复合和匿名反馈的多臂老虎机问题，提出适应性算法，解决了没有先验关于奖励间隔大小的信息的问题，并且进行了基于真实数据集的模拟实验，结果表明我们的算法胜过现有的基准算法。