To address the limitations of the existing image adversarial example generation methods that only applying global and uniform transformations within a single domain and thereby restricting the attack success rates and the transferability of adversarial examples， a Spatial-Frequency Collaborative adversarial example generation method based on Class Activation Mapping （CAM） （SFC-CAM） was proposed. Firstly， region sensitivity was quantified using CAM， and the input image was divided into high-sensitivity target region and low-sensitivity background region by Adaptive Partitioning （AP） according to the threshold of activation value. Then， for high-sensitivity region， Channel Resampling-Block-wise Random Scaling （CR-BRS） was applied in the spatial domain， while Discrete Cosine Transform （DCT） with Spectral Random Masking （DCT-SRM） was conducted in the frequency domain for low-sensitivity region. Finally， adversarial examples were generated on the basis of the average gradient of the co-transformed image iteratively. Experimental results on the ImageNet dataset show that with Inception-v3 as the source model， SFC-CAM improves the average attack success rate by 3.4 and 10.4 percentage points compared with the baseline methods — Channel Augmented Attack Method （CAAM） and Spectrum Simulation Attack （SSA）， respectively； compared with the proposed single-domain adversarial attack methods CR-BRS and DCT-SRM， SFC-CAM improves the average attack success rate by 15.9 and 19.7 percentage points， respectively. These verify that SFC-CAM enhances the diversity of surrogate model decision boundaries， thereby achieving model augmentation and improving the black-box attack success rate and transferability of adversarial examples.