An Effective and Robust Detector for Logo Detection

• URL: http://arxiv.org/abs/2108.00422v1
• Date: Sun, 1 Aug 2021 10:17:53 GMT
• Title: An Effective and Robust Detector for Logo Detection
• Authors: Xiaojun Jia, Huanqian Yan, Yonglin Wu, Xingxing Wei, Xiaochun Cao, Yong Zhang
• Abstract summary: Some attackers fool the well-trained logo detection model for infringement. A novel logo detector based on the mechanism of looking and thinking twice is proposed in this paper. We extend detectoRS algorithm to a cascade schema with an equalization loss function, multi-scale transformations, and adversarial data augmentation.
• Score: 58.448716977297565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid Missed Detection and False Alarm [10.135979083516174]
  Image copy-move is an operation that replaces one part of the image with another part of the same image, which can be used for illegal purposes. Recent studies have shown that keypoint-based algorithms achieved excellent and robust localization performance. However, when the input image is low-resolution, most existing keypoint-based algorithms are difficult to generate sufficient keypoints.
  arXiv  Detail & Related papers  (2024-06-05T13:50:29Z)
• PrObeD: Proactive Object Detection Wrapper [15.231600709902127]
  PrObeD consists of an encoder-decoder architecture, where the encoder network generates an image-dependent signal templates to encrypt the input images. We propose a wrapper based on proactive schemes, PrObeD, which enhances the performance of these object detectors by learning a signal. Our experiments on MS-COCO, CAMO, COD$10$K, and NC$4$K datasets show improvement over different detectors after applying PrObeD.
  arXiv  Detail & Related papers  (2023-10-28T19:25:01Z)
• A Cross-direction Task Decoupling Network for Small Logo Detection [28.505952002735334]
  We creatively propose Cross-direction Task Decoupling Network (CTDNet) for small logo detection. Comprehensive experiments on four logo datasets demonstrate the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2023-05-04T02:23:34Z)
• Context-Aware Transfer Attacks for Object Detection [51.65308857232767]
  We present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks.
  arXiv  Detail & Related papers  (2021-12-06T18:26:39Z)
• Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection [52.36825190893928]
  We propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA) Our approach mainly consists of Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA)
  arXiv  Detail & Related papers  (2021-08-31T11:59:00Z)
• Object Hider: Adversarial Patch Attack Against Object Detectors [10.920696684006488]
  In this paper, we focus on adversarial attack on some state-of-the-art object detection models. As a practical alternative, we use adversarial patches for the attack. Experiment results have shown that the proposed methods are highly effective, transferable and generic.
  arXiv  Detail & Related papers  (2020-10-28T13:34:16Z)
• DPAttack: Diffused Patch Attacks against Universal Object Detection [66.026630370248]
  Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. We propose a diffused patch attack (textbfDPAttack) to fool object detectors by diffused patches of asteroid-shaped or grid-shape. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches.
  arXiv  Detail & Related papers  (2020-10-16T04:48:24Z)
• LogoDet-3K: A Large-Scale Image Dataset for Logo Detection [61.296935298332606]
  We introduce LogoDet-3K, the largest logo detection dataset with full annotation. It has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. We propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the state-of-the-art YOLOv3 framework for large-scale logo detection.
  arXiv  Detail & Related papers  (2020-08-12T14:57:53Z)
• Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond [97.25179345878443]
  This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB) AWB can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels. Experiments demonstrate that the proposed method achieves state-of-the-art performance with significant improvements on multiple UDA person re-identification tasks.
  arXiv  Detail & Related papers  (2020-06-11T15:40:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.