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Your search keyword '"Bullock, Leon Bevan"' showing total 7 results
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7 results on '"Bullock, Leon Bevan"'

1. SATBA: An Invisible Backdoor Attack Based On Spatial Attention

Author

Zhou, Huasong, Xu, Xiaowei, Wang, Xiaodong, and Bullock, Leon Bevan

Subjects
Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract

Backdoor attack has emerged as a novel and concerning threat to AI security. These attacks involve the training of Deep Neural Network (DNN) on datasets that contain hidden trigger patterns. Although the poisoned model behaves normally on benign samples, it exhibits abnormal behavior on samples containing the trigger pattern. However, most existing backdoor attacks suffer from two significant drawbacks: their trigger patterns are visible and easy to detect by backdoor defense or even human inspection, and their injection process results in the loss of natural sample features and trigger patterns, thereby reducing the attack success rate and model accuracy. In this paper, we propose a novel backdoor attack named SATBA that overcomes these limitations using spatial attention and an U-net based model. The attack process begins by using spatial attention to extract meaningful data features and generate trigger patterns associated with clean images. Then, an U-shaped model is used to embed these trigger patterns into the original data without causing noticeable feature loss. We evaluate our attack on three prominent image classification DNN across three standard datasets. The results demonstrate that SATBA achieves high attack success rate while maintaining robustness against backdoor defenses. Furthermore, we conduct extensive image similarity experiments to emphasize the stealthiness of our attack strategy. Overall, SATBA presents a promising approach to backdoor attack, addressing the shortcomings of previous methods and showcasing its effectiveness in evading detection and maintaining high attack success rate., Comment: 9 pages, 9 figures

Published
2023

5. PanopticUAV: Panoptic Segmentation of UAV Images for Marine Environment Monitoring.

Author

Yuling Dou, Fengqin Yao, Xiandong Wang, Liang Qu, Long Chen, Zhiwei Xu, Laihui Ding, Bullock, Leon Bevan, Guoqiang Zhong, and Shengke Wang

Subjects
MARINE resources conservation, IMAGE segmentation, LAPLACIAN operator, OPERATOR equations, IMAGE recognition (Computer vision), SPARTINA alterniflora, OIL spills
Abstract

UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on UAV marine image recognition with panoptic segmentation. In addition, there are no publicly available panoptic segmentation datasets for UAV images. In this work, we collect and annotate UAV images to form a panoptic segmentation UAV dataset named UAV-OUC-SEG and propose a panoptic segmentation method named PanopticUAV. First, to deal with the large intraclass variability in scale, deformable convolution and CBAM attention mechanism are employed in the backbone to obtain more accurate features. Second, due to the complexity and diversity of marine images, boundary masks by the Laplacian operator equation from the ground truth are merged into feature maps to improve boundary segmentation precision. Experiments demonstrate the advantages of PanopticUAV beyond the most other advanced approaches on the UAV-OUC-SEG dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Token-Selective Vision Transformer for fine-grained image recognition of marine organisms

Author

Si, Guangzhe, Xiao, Ying, Wei, Bin, Bullock, Leon Bevan, Wang, Yueyue, Wang, Xiaodong, Si, Guangzhe, Xiao, Ying, Wei, Bin, Bullock, Leon Bevan, Wang, Yueyue, and Wang, Xiaodong

Abstract

IntroductionThe objective of fine-grained image classification on marine organisms is to distinguish the subtle variations in the organisms so as to accurately classify them into subcategories. The key to accurate classification is to locate the distinguishing feature regions, such as the fish's eye, fins, or tail, etc. Images of marine organisms are hard to work with as they are often taken from multiple angles and contain different scenes, additionally they usually have complex backgrounds and often contain human or other distractions, all of which makes it difficult to focus on the marine organism itself and identify its most distinctive features. Related workMost existing fine-grained image classification methods based on Convolutional Neural Networks (CNN) cannot accurately enough locate the distinguishing feature regions, and the identified regions also contain a large amount of background data. Vision Transformer (ViT) has strong global information capturing abilities and gives strong performances in traditional classification tasks. The core of ViT, is a Multi-Head Self-Attention mechanism (MSA) which first establishes a connection between different patch tokens in a pair of images, then combines all the information of the tokens for classification. MethodsHowever, not all tokens are conducive to fine-grained classification, many of them contain extraneous data (noise). We hope to eliminate the influence of interfering tokens such as background data on the identification of marine organisms, and then gradually narrow down the local feature area to accurately determine the distinctive features. To this end, this paper put forwards a novel Transformer-based framework, namely Token-Selective Vision Transformer (TSVT), in which the Token-Selective Self-Attention (TSSA) is proposed to select the discriminating important tokens for attention computation which helps limits the attention to more precise local regions. TSSA is applied to different layers, and the num

Published
2023

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