Ruowei Wang

Bio: Ruowei Wang is an academic researcher from Sichuan University. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 1, co-authored 3 publications receiving 2 citations.

Improved Data Augmentation of Deep Convolutional Neural Network for Pollen Grains Classification

Abstract: Traditionally, it is a time-consuming work for experts to accomplish pollen grains classification. With the popularity of deep Convolutional Neural Network (CNN) in computer vision, many automatic pollen grains classification methods based on CNN have been proposed in recent years. However, The CNN they used often focus on the most proniment area in the center of pollen grains and neglect the less discriminative local features in the surrounding of pollen grains. In order to alleviate this situation, we propose two data augmentation operations. Our experiment results on Pollen13K achieve a weighted F1 score of 97.26% and an accuracy of 97.29%.

Watermark Faker: Towards Forgery of Digital Image Watermarking

Abstract: Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.

Watermark Faker: Towards Forgery of Digital Image Watermarking

Abstract: Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open issues that whether it is possible to generate fake watermarked images for circumvention. In this paper, we make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Suppose that a set of paired images of original and watermarked images generated by the targeted watermarker are available, we use them to train a watermark faker with U-Net as the backbone, whose input is an original image, and after a domain-specific preprocessing, it outputs a fake watermarked image. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains, suggesting the risk of such forgery attacks.

Fine-Grained Image Classification for Pollen Grain Microscope Images

Abstract: Pollen classification is an important task in many fields, including allergology, archaeobotany and biodiversity conservation. However, the visual classification of pollen grains is a major challenge due to the difficulty in identifying the subtle variations between the sub-categories of objects. The pollen image analysis process is often time-consuming and require expert evaluations. Even simple tasks, such as image classification or segmentation requires significant efforts from experts in aerobiology. Hence, there is a strong need to develop automatic solutions for microscopy image analysis. These considerations underline the effort to study and develop new efficient algorithms. With the growing interest in Deep Learning (DL), much research efforts have been spent to the development of several approaches to accomplish this task. Hence, this study covers the application of effective Deep Learning methods in combination with Fine-Grained Visual Classification (FGVC) approaches, comparing them with other Deep Learning-based methods from the state-of-art. All experiments were conducted using the dataset Pollen13K, composed of more than 13,000 pollen objects subdivided in 4 classes. The results of experiments confirmed the effectiveness of our proposed pipeline that reached over 97% in terms of accuracy and F1-score.

A Dual-channel Attention Model for Optical Microscope Pollen Classification

Abstract: The effective classification of pollen is a critical method to the prevention of pollen allergy. The conventional pollen classification primarily relies on manual handling under the microscope, which does not only require a lot of manpower but also has low classicization accuracy on results. In this paper, the optical microscope was used to scan the slides and the pollen image dataset was made for classification. We found from the pollen dataset that most of the images have clear contours, but there were also many pollen images with the following problems. One is that the pollen was covered by impurities like dust and pebbles, and the other is that the pollen images stain unevenly and the pollen image was blurred and the number of pollens was unbalanced. This paper proposes a Dual-Channel Attention method, we call it DCANet, which combines the advantages of channel attention and channel self-attention to improve the classification accuracy. The results of classification activation map analysis showed that DCANet paid more attention to pollen images.

A Dual-channel Attention Model for Optical Microscope Pollen Classification

Abstract: The effective classification of pollen is a critical method to the prevention of pollen allergy. The conventional pollen classification primarily relies on manual handling under the microscope, which does not only require a lot of manpower but also has low classicization accuracy on results. In this paper, the optical microscope was used to scan the slides and the pollen image dataset was made for classification. We found from the pollen dataset that most of the images have clear contours, but there were also many pollen images with the following problems. One is that the pollen was covered by impurities like dust and pebbles, and the other is that the pollen images stain unevenly and the pollen image was blurred and the number of pollens was unbalanced. This paper proposes a Dual-Channel Attention method, we call it DCANet, which combines the advantages of channel attention and channel self-attention to improve the classification accuracy. The results of classification activation map analysis showed that DCANet paid more attention to pollen images.