CMBA:Backdoor Attack of Neural Networks Based on Complex Mapping
Most existing research on backdoor attacks has primarily focused on simple backdoor mapping strategies,such as all-to-one mapping.This approach overlooks the need for more complex mapping strategies in real-world attack scenarios,thereby limiting the flexibility and effectiveness of backdoor attacks.To solve the problem,this article proposes a backdoor attack framework with an adjustable mapping strategy,and implements a backdoor attack based on complex mapping,called CMBA.By introducing evasion classes and the multi-target setting,CMBA can precisely control the attack scope,and establish complex correspondences between different original classes and multiple target classes,thereby improving the flexibility of attacks.The experimental results show that CMBA performs well on three datasets.In addition,by introducing more evasion classes and target classes,CMBA becomes stealthier and successfully bypasses the detection of several current mainstream backdoor defenses.
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