Selective Consistency Gradient Attack: Resolving Multi-Target Gradient Conflicts in Object Detection

Adversarial attack adds an imperceptible perturbation on images to fool a model. Though existing adversarial attack methods have demonstrated great success in image classification tasks, they suffer inferior attack performances on object detection. We find that there exists multi-target gradient conflict (MGC) between different targets during attack process in object detection. To tackle this issue, we propose an effective pixel-level attack method, namely Selective Consistency Gradient Attack (SCGA). First, we select a dominant gradient direction by gradient ranking. Then, we introduce the gradient conflict rate to select targets with consistent gradients to improve attack efficiency. Finally, the perturbation is generated by gradient merging. Experiments on COCO 2017 validation subset verify the effectiveness of SCGA on both white-box attack and black-box attack, outperforming other methods with a large margin.