 This study proposes a sample augmentation scheme based on generative adversarial networks to generate high-quality SAR deception jamming templates with shadows, addressing the low authenticity and low similarity issues of current sample augmentation schemes. The proposed scheme uses a channel attention mechanism module to improve the learning ability of shadow features and considers speckle noise in the network to avoid reduced authenticity. The comparison results show that the proposed scheme generates more authentic SAR deception jamming templates with targets and shadow features similar to those of the original image, which can achieve fast and effective SAR deception jamming. This article was authored by Shrenan Lang, Goetian Li, Yilu, and others.