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in this work we investigate how to defend a diversary attack with camera image Pipelines adversal attack and defense is an important topic in computer vision existing new networks have shown to be vulnerable to the virtual attack where aiding imperceptual perturbations to the input images can follow the new network into making our own predictions for example for the Black Swan image here we can find certain noise such as adding this noise can drive the new network to classify the image as the airline the existence of the virtual examples pull thrust to the reliability of the new network especially for the safety critical applications so depending against such adversal examples is crucial existing defense methods fall into two types adversal training and input transformation The Universal Training Method generate a virtual examples iteratively in the training phrase and then use these examples to strengthen the model this approach however is extremely costly in training time and moreove
