Knowledge Commons of Institute of Automation,CAS
Robust Image Classification with Cognitive-Driven Color Priors | |
Gu, Peng1,2; Zhu, Chengfei2; Lan, Xiaosong2,3; Wang, Jie1,2; Li, Shuxiao1,2 | |
发表期刊 | ELECTRONICS |
2020-11-01 | |
卷号 | 9期号:11页码:16 |
通讯作者 | Li, Shuxiao(shuxiao.li@ia.ac.cn) |
摘要 | Existing image classification methods based on convolutional neural networks usually use a large number of samples to learn classification features hierarchically, causing the problems of over-fitting and error propagation layer by layer. Thus, they are vulnerable to adversarial samples generated by adding imperceptible disturbances to input samples. To address the above issue, we propose a cognitive-driven color prior model to memorize the color attributes of target samples inspired by the characteristics of human memory. At inference stage, color priors are indexed from the memory and fused with features of convolutional neural networks to achieve robust image classification. The proposed color prior model is cognitive-driven and has no training parameters, thus it has strong generalization and can effectively defend against adversarial samples. In addition, our method directly combines the features of the prior model with the classification probability of the convolutional neural network, without changing the network structure and its parameters of the existing algorithm. It can be combined with other adversarial attack defense methods, such as various preprocessing modules such as PixelDefense or adversarial training methods, to improve the robustness of image classification. Experiments on several benchmark datasets show that the proposed method improves the anti-interference ability of image classification algorithms. |
关键词 | adversarial samples color prior model image classifification |
DOI | 10.3390/electronics9111837 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[U19B2033] ; National Key RD Program[2019YFF0301801] ; Frontier Science and Technology Innovation Project[2019QY2404] ; Innovation Academy for Light-Duty Gas Turbine, Chinese Academy of Sciences[CXYJJ19-ZD-02] |
项目资助者 | National Natural Science Foundation of China ; National Key RD Program ; Frontier Science and Technology Innovation Project ; Innovation Academy for Light-Duty Gas Turbine, Chinese Academy of Sciences |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000592928700001 |
出版者 | MDPI |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41662 |
专题 | 多模态人工智能系统全国重点实验室_脑机融合与认知评估 |
通讯作者 | Li, Shuxiao |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Innovat Acad Light Duty Gas Turbine, Beijing 100080, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gu, Peng,Zhu, Chengfei,Lan, Xiaosong,et al. Robust Image Classification with Cognitive-Driven Color Priors[J]. ELECTRONICS,2020,9(11):16. |
APA | Gu, Peng,Zhu, Chengfei,Lan, Xiaosong,Wang, Jie,&Li, Shuxiao.(2020).Robust Image Classification with Cognitive-Driven Color Priors.ELECTRONICS,9(11),16. |
MLA | Gu, Peng,et al."Robust Image Classification with Cognitive-Driven Color Priors".ELECTRONICS 9.11(2020):16. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论