Titlebook: Attacks, Defenses and Testing for Deep Learning; Jinyin Chen,Ximin Zhang,Haibin Zheng Book 2024 The Editor(s) (if applicable) and The Auth

確定的事

Lipohypertrophy

胖人手藝好

Neuron-Level Inverse Perturbation Against Adversarial Attackson, especially when deployed in security-critical domains. Numerous defense methods, including reactive and proactive ones, have been proposed for model robustness improvement. The former ones, such as conducting transformations to remove perturbations, usually fail to handle large perturbations via

少量

光亮

Defense Against Free-Rider Attack from?the?Weight Evolving Frequencyuted machine learning. Although federated learning has gained an unprecedented success in data privacy preservation, its frailty of vulnerability to “free-rider” attacks attracts increasing attention. A number of defenses against free-rider attacks have been proposed for FL. Nevertheless, these meth

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An Effective Model Copyright Protection for?Federated Learning its excellent performance and significant profits, it has been applied to a wide range of practical areas. . has become a major issue. It is possible that FL could benefit from the existing property rights protection methods in centralized scenarios, such as watermark embedding and model fingerprin

統(tǒng)治人類

忍受

Using Adversarial Examples to against Backdoor Attack in Federated Learningared global model. Unluckily, by uploading a carefully crafted updated model, a malicious client can insert a backdoor into the global model during federated learning training. Many secure aggregation policies and robust training protocols have been proposed to protect against backdoor attacks in FL

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