To address the problems that the communication messages of Automatic Dependent Surveillance-Broadcast （ADS-B） system lack encryption and identity authentication mechanisms and are vulnerable to spoofing attacks such as replay and trajectory forgery， but traditional detecting models cannot identify anomalies in the system， an ADS-B attack detection method based on an improved DeepLabV3+ model was proposed. First， the original tabular ADS-B data were reconstructed into grayscale images through grayscale mapping， thereby enabling the visualization of spatio-temporal features. Then， to overcome the weak feature extraction capability and blurred segmentation boundaries of DeepLabV3+ model when processing ADS-B image data， a Haar Wavelet Downsampling （HWD） module was used to replace the max-pooling operation in the ResNet101 backbone network， thereby preserving key features such as trajectory anomalies and local perturbations during dimensionality reduction effectively. Finally， a Simple parameter-free Attention Mechanism （SimAM） was introduced after the ADS-B low-level feature map to enhance the representation of local spatial structures， and a Fusion Convolutional Block Attention Module （F_CBAM） was incorporated after the Atrous Spatial Pyramid Pooling （ASPP） module to highlight critical semantic regions through the parallel interaction of channel and spatial attentions. Experimental results on an ADS-B dataset show that compared with the baseline DeepLabV3+ model， the proposed method achieves a 6.73 percentage points improvement in mean Intersection over Union （mIoU）. In complex attack scenarios， the proposed method has the F1-scores for Denial of Service （DoS） attacks and random noise reached 96.07% and 92.55%， respectively， which are significantly higher than those of mainstream models such as U-Net （U-shaped convolutional Network）， SegFormer （Segmentation Transformer）， and K-Net （Kernel Network）， providing a new approach for accurate detection of ADS-B attacks.