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During the generation of invisible backdoor attack poisoned data, the featurespace transformation operation tends to cause the loss of some poisonedfeatures and weakens the mapping relationship between source images withtriggers and target labels, resulting in the need for a higher poisoning rateto achieve the corresponding backdoor attack success rate. To solve the aboveproblems, we propose the idea of feature repair for the first time andintroduce the blind watermark technique to repair the poisoned features lostduring the generation of poisoned data. Under the premise of ensuringconsistent labeling, we propose a low-poisoning rate invisible backdoor attackbased on feature repair, named FRIB. Benefiting from the above design concept,the new method enhances the mapping relationship between the source images withtriggers and the target labels, and increases the degree of misleading DNNs,thus achieving a high backdoor attack success rate with a very low poisoningrate. Ultimately, the detailed experimental results show that the goal ofachieving a high success rate of backdoor attacks with a very low poisoningrate is achieved on all MNIST, CIFAR10, GTSRB, and ImageNet datasets.

FRIB: Low-poisoning Rate Invisible Backdoor Attack based on Feature Repair