The robustness of adversarial examples to image scaling transformation is usually ignored when most existing adversarial attacks are proposed. In contrast, image scaling is often the first step of the model to transfer various sizes of input images into fixed ones. We evaluate the impact of image scaling on the robustness of adversarial examples applied to image classification tasks. We set up an image scaling system to provide a basis for robustness evaluation and conduct experiments in different situations to explore the relationship between image scaling and the robustness of adversarial examples. Experiment results show that various scaling algorithms have a similar impact on the robustness of adversarial examples, but the scaling ratio significantly impacts it.

Retailers advertise on different sales channels. When consumers buy online, the effectiveness of advertising is discounted because they cannot feel the product. Observing this phenomenon, this paper studies the advertising strategy of a dual-channel retailer. In this paper, we build stylized game models for the retailer’s price and advertising levels on online and offline channels, respectively. Our contribution is to provide prescriptions for how dual-channel retailers make price and advertise decisions and determine which channel is more profitable for retailers. We find that advertising discounts are not always harming the retailer’s profits. The level of advertising discounts causes retailers to increase the selling price when consumers engage in online shopping. Also, we derive that retailers can choose the sales channels based on the level of consumers’ channel preference of consumers. Interestingly, an increase in the level of advertising discounts will contribute to the growth of the retailer’s profit. Finally, by numerical analysis, we demonstrate the robustness of the results.