In this work, we propose an enhanced adversarial attack based on DAG, to improve the transferability of adversarial example to fool CNN-based object detectors. DAG achieves excellent performance under white-box settings via attacking the detection head, but has poor transferability under black-box cases. We jointly attack the feature map of backbone and the detection head to improve the transferability. Because, we think the attack against high-level feature of backbone can transfer to disturb other homogeneous CNN-based backbones. To this end, we optimize the combination of feature loss of backbone and classification loss of region proposals to generate adversarial examples. Extensive experiments on PASCAL VOC and COCO datasets demonstrate that our attack can transfer to attack the detector with different backbone or different pipelines even under defense situations, and has achieved superior transferability than state-of-the-arts.