  In recent years, intellectual property (IP), which represents literary,
inventions, artistic works, etc, gradually attract more and more people's
attention. Particularly, with the rise of e-commerce, the IP not only
represents the product design and brands, but also represents the images/videos
displayed on e-commerce platforms. Unfortunately, some attackers adopt some
adversarial methods to fool the well-trained logo detection model for
infringement. To overcome this problem, a novel logo detector based on the
mechanism of looking and thinking twice is proposed in this paper for robust
logo detection. The proposed detector is different from other mainstream
detectors, which can effectively detect small objects, long-tail objects, and
is robust to adversarial images. In detail, we extend detectoRS algorithm to a
cascade schema with an equalization loss function, multi-scale transformations,
and adversarial data augmentation. A series of experimental results have shown
that the proposed method can effectively improve the robustness of the
detection model. Moreover, we have applied the proposed methods to competition
ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi
platform and won top 2 in 36489 teams. Code is available at
https://github.com/jiaxiaojunQAQ/Robust-Logo-Detection.
