To address the issues of transferability, stealthiness, and attack effectiveness in attacking scene text detection algorithms, we proposed a MIFGSM-W attack algorithm. The MIFGSM-W attack algorithm introduced a general probability map and a momentum term to obtain stable gradient update directions. Differentiable functions were used instead of standard binarization functions to construct the loss function. Variable was introduced, and an optimization strategy was proposed to improve variable and constrain the perturba-tion. Individual attack algorithms and a general attack algorithm were proposed. The results of our experiments on multiple datasets demonstrate that the proposed attack algorithm successfully targets scene text detection models such as EAST, Textbox+ +, Craft, and DBNet. Moreover, the generated adversarial samples exhibit both transferability and visual stealthiness.
关键词
场景文本检测/对抗样本/MIFGSM-W攻击算法/迁移性
Key words
scene text detection/adversarial examples/MIFGSM-W attack algorithm/transferability
维普
万方数据