Liu et al. propose DPatch, adversarial patches against state-of-the-art object detectors. Similar to existing adversarial patches, where a patch with fixed pixels is placed in an image in order to evade (or change) classification, the authors compute their DPatch using an optimization procedure. During optimization, the patch to be optimized is placed in random locations on all images of, e.g. on PASCAL VOC 2007, and the pixels are updated in order to maximize the loss of the classifier (either in a targeted setting or in an untargeted setting). In experiments, this approach is able to fool several different detectors. Using small $40\times40$ pixel patches as illustrated in Figure 1.

https://i.imgur.com/ma6hGNO.jpg
Figure 1: Illustration of the use case of DPatch.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Goldblum et al. show that distilling robustness is possible, however, depends on the teacher model and the considered dataset. Specifically, while classical knowledge distillation does not convey robustness against adversarial examples, distillation with a robust teacher model might increase robustness of the student model – even if trained on clean examples only. However, this seems to depend on both the dataset as well as the teacher model, as pointed out in experiments on Cifar100. Unfortunately, from the paper, it does not become clear in which cases robustness distillation does not work. To overcome this limitation, the authors propose to combine adversarial training and distillation and show that this recovers robustness; the student model’s robustness might even exceed the teacher model’s robustness. This, however, might be due to the additional adversarial examples used during distillation.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).