Machine learning has been pervasively used in a wide range of applications due to its technical breakthroughs in recent years. It has demonstrated significant success in dealing with various complex problems, and shows capabilities close to humans or even beyond humans. However, recent studies show that machine learning models are vulnerable to various attacks, which will compromise the security of the models themselves and the application systems. Moreover, such attacks are stealthy due to the unexplained nature of the deep learning models. In this survey, we systematically analyze the security issues of machine learning, focusing on existing attacks on machine learning systems, corresponding defenses or secure learning techniques, and security evaluation methods. Instead of focusing on one stage or one type of attack, this paper covers all the aspects of machine learning security from the training phase to the test phase. First, the machine learning model in the presence of adversaries is presented, and the reasons why machine learning can be attacked are analyzed. Then, the machine learning security-related issues are classified into five categories: training set poisoning; backdoors in the training set; adversarial example attacks; model theft; recovery of sensitive training data. The threat models, attack approaches, and defense techniques are analyzed systematically. To demonstrate that these threats are real concerns in the physical world, we also reviewed the attacks in real-world conditions. Several suggestions on security evaluations of machine learning systems are also provided. Last, future directions for machine learning security are also presented. INDEX TERMS Artificial intelligence security, poisoning attacks, backdoor attacks, adversarial examples, privacy-preserving machine learning.
The high-temperature compression characteristics of a Ti-55511 alloy are explored through adopting two-stage high-temperature compressed experiments with step-like strain rates. The evolving features of dislocation substructures over hot, compressed parameters are revealed by transmission electron microscopy (TEM). The experiment results suggest that the dislocations annihilation through the rearrangement/interaction of dislocations is aggravated with the increase in forming temperature. Notwithstanding, the generation/interlacing of dislocations exhibit an enhanced trend with the increase in strain in the first stage of forming, or in strain rates at first/second stages of a high-temperature compressed process. According to the testing data, an Informer deep learning model is proposed for reconstructing the stress–strain behavior of the researched Ti-55511 alloy. The input series of the established Informer deep learning model are compression parameters (compressed temperature, strain, as well as strain rate), and the output series are true stresses. The optimal input batch size and sequence length are 64 and 2, respectively. Eventually, the predicted results of the proposed Informer deep learning model are more accordant with the tested true stresses compared to those of the previously established physical mechanism model, demonstrating that the Informer deep learning model enjoys an outstanding forecasted capability for precisely reconstructing the high-temperature compressed features of the Ti-55511 alloy.
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