Adam algorithm-based adversarial sample generation method and system
A technology against samples and algorithms, applied in neural learning methods, calculations, computer components, etc., can solve unrealistic problems, achieve the effects of ensuring accuracy and efficiency, improving migration, and improving generation quality
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[0027] In order to make the purpose, technical solution and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and technical solutions.
[0028] The convolutional neural network has reached a level beyond human beings in image recognition tasks, but it is still vulnerable to the attack of adversarial samples. Since adversarial samples are noise invisible to human vision added on the basis of the original image, its existence will give Deep learning systems pose potential security threats. Adversarial examples with strong attack performance can also be used as an important tool to evaluate the robustness of models. However, in the case of black boxes, the attack success rate of adversarial samples needs to be improved. An embodiment of the present invention provides a method for generating an adversarial example based on the Adam algorithm, see figure ...
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