Corpus ID: 220793720

MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation

@article{Yan2020MirrorNetBA,
  title={MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation},
  author={Jinnan Yan and Trung-Nghia Le and Khanh-Duy Nguyen and Minh-Triet Tran and Thanh-Toan Do and Tam V. Nguyen},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.12881}
}
Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and adversarial attack for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the adversarial stream corresponding with the original image and its… Expand
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References

SHOWING 1-10 OF 62 REFERENCES
Adversarial Examples for Semantic Segmentation and Object Detection
TLDR
This paper proposes a novel algorithm named Dense Adversary Generation (DAG), which applies to the state-of-the-art networks for segmentation and detection, and finds that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks. Expand
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
TLDR
This paper presents what to their knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets and shows how mean-field inference in deep structured models and multiscale processing naturally implement recently proposed adversarial defenses. Expand
Anabranch network for camouflaged object segmentation
TLDR
This paper proposes a general end-to-end network, called the Anabranch Network, that leverages both classification and segmentation tasks and possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image. Expand
Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
TLDR
This paper presents a framework for discovering DNN failures that harnesses 3D renderers and 3D models, and investigates the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. Expand
Detection of People With Camouflage Pattern Via Dense Deconvolution Network
TLDR
Experimental results demonstrate that the proposed method outperforms the classical camouflaged object detection method and typical CNN-based detection methods. Expand
Camouflaged Object Detection
TLDR
A simple but effective framework for COD, termed Search Identification Network (SINet), which outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Expand
BASNet: Boundary-Aware Salient Object Detection
TLDR
Experimental results on six public datasets show that the proposed predict-refine architecture, BASNet, outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures. Expand
Mask R-CNN
TLDR
This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. Expand
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
  • Nian Liu, Junwei Han
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
Evaluations on four benchmark datasets and comparisons with other 11 state-of-the-art algorithms demonstrate that DHSNet not only shows its significant superiority in terms of performance, but also achieves a real-time speed of 23 FPS on modern GPUs. Expand
Performance of Decamouflaging Through Exploratory Image Analysis
TLDR
This paper has proposed a system to identify the camouflaged object and to extract that from the background efficiently, which is needed for the authors' country to save soldiers in defense area. Expand
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