| 期刊全称 | Attacks, Defenses and Testing for Deep Learning | | 影响因子2023 | Jinyin Chen,Ximin Zhang,Haibin Zheng | | 视频video | | | 发行地址 | The security problems of different data modes, different model structures and different tasks are fully considered.The attack problems are comprehensively studied, and the system flow of the attack-de | | 图书封面 |  | | 影响因子 | .This book provides a systematic study on the security of deep learning. With its powerful learning ability, deep learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that deep learning is vulnerable to malicious attacks, which will lead to unpredictable consequences. Take autonomous driving as an example, there were more than 12 serious autonomous driving accidents in the world in 2018, including Uber, Tesla and other high technological enterprises. Drawing on the reviewed literature, we need to discover vulnerabilities in deep learning through attacks, reinforce its defense, and test model performance to ensure its robustness. ..Attacks can be divided into adversarial attacks and poisoning attacks. Adversarial attacks occur during the model testing phase, where the attacker obtains adversarial examples by adding small perturbations. Poisoning attacks occur during the model training phase, wherethe attacker injects poisoned examples into the training dataset, embedding a backdoor trigger in the trained deep learning model. ..An effective defense method is an important guarantee for the application of | | Pindex | Book 2024 |
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书目名称Attacks, Defenses and Testing for Deep Learning影响因子(影响力)学科排名
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发表于 2025-3-21 19:32:06
