Tongan Yu1, Yali Xue1,*, Yiming He1, Shan Cui2, Jun Hong2
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.056075
- 15 October 2024
Abstract With the rapid development of deep learning-based detection algorithms, deep learning is widely used in the field of infrared small target detection. However, well-designed adversarial samples can fool human visual perception, directly causing a serious decline in the detection quality of the recognition model. In this paper, an adversarial defense technology for small infrared targets is proposed to improve model robustness. The adversarial samples with strong migration can not only improve the generalization of defense technology, but also save the training cost. Therefore, this study adopts the concept of maximizing multidimensional feature distortion, applying noise… More >
Login
Register
