Abstract: To realize fast and effective synthetic aperture radar (SAR) deception jamming, it is essential to generate a high-quality library of SAR deception jamming templates. This can be achieved through the sample augmentation of SAR deception jamming templates. Sample augmentation refers to the process of artificially generating new templates by applying various techniques to existing SAR templates, thereby expanding the available data and improving the robustness of the jamming process. However, current sample augmentation schemes for SAR deception jamming templates face several challenges. One major issue is the low authenticity of the generated templates, primarily due to the absence of realistic speckle noise. Speckle noise, typically caused by random interference in the radar signal, is an inherent characteristic of SAR imagery. Without this noise, the generated templates fail to accurately mimic real SAR data. Another significant issue is the low similarity between the generated templates and the target and shadow areas in the input templates. These regions are critical for SAR jamming, as they contribute to the overall realism of the jamming effect. Low similarity in these regions can reduce the effectiveness of jamming and make it easier for the adversary to detect the deception. To address these challenges, this study proposes an advanced sample augmentation scheme based on generative adversarial networks (GANs), which are capable of generating high-quality SAR deception jamming templates with realistic shadows. The GANs are a class of deep learning models known for their ability to generate new data by learning from existing data, making them well-suited for sample augmentation tasks. The proposed scheme addresses two primary issues. First, the influence of speckle noise is incorporated into the network architecture, enabling the model to generate images that closely resemble real SAR imagery. This inclusion mitigates the problem of low authenticity in the generated templates and enhances their realism. Second, a channel attention mechanism module is integrated into the GAN architecture to improve the model's ability to focus on and learn shadow features. Shadows play a vital role in defining the structure and appearance of SAR images. The attention mechanism ensures that the model focuses more effectively on shadow regions during the learning process. As a result, the generated templates exhibit higher similarity to the target and shadow areas in the input templates, thereby improving the overall quality of the augmented samples. To evaluate the performance of the proposed scheme, a comparative analysis was conducted between the SinGAN—a GAN designed for image sample augmentation from a single SAR image—denoising diffusion probabilistic models (DDPMs), which are generative models that reverse a gradual noising process to produce high-quality samples, and the proposed method. The evaluation was based on three key metrics: the equivalent number of looks (ENL), the correlation coefficient, and the gradient-based structural similarity (GSSIM) between the target and shadow regions in the augmented templates. These metrics were chosen to evaluate the quality, realism, and similarity of the generated templates. The comparison results demonstrated that the templates generated by the proposed scheme outperformed those produced by SinGAN and DDPMs in terms of authenticity and similarity to the original target and shadow regions. Specifically, the proposed scheme generated templates that more closely resembled the real SAR images by effectively incorporating both speckle noise and shadow features. These enhancements make the proposed approach more suitable for fast and effective SAR deception jamming, offering a robust tool for improving the realism and operational effectiveness of SAR-based jamming strategies.