We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.
Deep Neural Networks have been found vulnerable re-cently. A kind of well-designed inputs, which called adver-sarial examples, can lead the networks to make incorrectpredictions. Depending on the different scenarios, goalsand capabilities, the difficulties of the attacks are different.For example, a targeted attack is more difficult than a non-targeted attack, a universal attack is more difficult than anon-universal attack, a transferable attack is more difficultthan a nontransferable one. The question is: Is there existan attack that can meet all these requirements? In this pa-per, we answer this question by producing a kind of attacksunder these conditions. We learn a universal mapping tomap the sources to the adversarial examples. These exam-ples can fool classification networks to classify all of theminto one targeted class, and also have strong transferability.Our code is released at: xxxxx.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.11332")
get_code_for_paper("1908.11332")
have("1908.11332")
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