2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-6.4
Paper Title Improving the Robustness of Convolutional Neural Networks via Sketch Attention
Authors Tianshu Chu, Zuopeng Yang, Jie Yang, Xiaolin Huang, Shanghai Jiao Tong University, China
SessionMLR-APPL-IP-6: Machine learning for image processing 6
LocationArea E
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The convolutional neural networks (CNNs) is biased towards texture while human eyes relying heavily on the general structure. The inconformity leads to the vulnerability of CNNs. The convolutional results is determined by the local patterns and delicate adversarial perturbation would be amplified layer-wise. Meanwhile the image context and object structure, which can be represented by sketch, stay almost unchanged. Therefore we propose that the sketch information is weak but more robust. In order to transfer the robustness from sketch to image and improve the capture of global structure, a sketch attention guided CNNs (SAG-CNNs) pipeline is constructed. The experiments on CIFAR-10 demonstrate that the defensive capability against black-box attacks of SAG-CNNs outperforms other counterparts evidently and achieve preferable trade-off between generalization and robustness.