As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to t...As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to the original image,which is inconceivable to the human but produces a massive error in the deep learning model.Existing attack methods have achieved good results when the network structure is known.However,in the case of unknown network structures,the effectiveness of the attacks still needs to be improved.Therefore,transfer-based attacks are now very popular because of their convenience and practicality,allowing adversarial samples generated on known models to be used in attacks on unknown models.In this paper,we extract sensitive features by Grad-CAM and propose two single-step attacks methods and a multi-step attack method to corrupt sensitive features.In two single-step attacks,one corrupts the features extracted from a single model and the other corrupts the features extracted from multiple models.In multi-step attack,our method improves the existing attack method,thus enhancing the adversarial sample transferability to achieve better results on unknown models.Our method is also validated on CIFAR-10 and MINST,and achieves a 1%-3%improvement in transferability.展开更多
News recommendation system is designed to deal with massive news and provide personalized recommendations for users.Accurately capturing user preferences and modeling news and users is the key to news recommendation.I...News recommendation system is designed to deal with massive news and provide personalized recommendations for users.Accurately capturing user preferences and modeling news and users is the key to news recommendation.In this paper,we propose a new framework,news recommendation system based on topic embedding and knowledge embedding(NRTK).NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding:1)extracting explicit and latent topic features from news and mining users’preferences for them in historical behaviors;2)extracting entities and propagating users’potential preferences in the knowledge graph.Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.展开更多
基金Supported by the Key R&D Projects in Hubei Province(2022BAA041 and 2021BCA124)the Open Foundation of Engineering Research Center of Cyberspace(KJAQ202112002)。
文摘As deep learning models have made remarkable strides in numerous fields,a variety of adversarial attack methods have emerged to interfere with deep learning models.Adversarial examples apply a minute perturbation to the original image,which is inconceivable to the human but produces a massive error in the deep learning model.Existing attack methods have achieved good results when the network structure is known.However,in the case of unknown network structures,the effectiveness of the attacks still needs to be improved.Therefore,transfer-based attacks are now very popular because of their convenience and practicality,allowing adversarial samples generated on known models to be used in attacks on unknown models.In this paper,we extract sensitive features by Grad-CAM and propose two single-step attacks methods and a multi-step attack method to corrupt sensitive features.In two single-step attacks,one corrupts the features extracted from a single model and the other corrupts the features extracted from multiple models.In multi-step attack,our method improves the existing attack method,thus enhancing the adversarial sample transferability to achieve better results on unknown models.Our method is also validated on CIFAR-10 and MINST,and achieves a 1%-3%improvement in transferability.
基金Supported by the Key Research&Development Projects in Hubei Province(2022BAA041 and 2021BCA124)the Open Foundation of Engineering Research Center of Cyberspace(KJAQ202112002)。
文摘News recommendation system is designed to deal with massive news and provide personalized recommendations for users.Accurately capturing user preferences and modeling news and users is the key to news recommendation.In this paper,we propose a new framework,news recommendation system based on topic embedding and knowledge embedding(NRTK).NRTK handle news titles that users have clicked on from two perspectives to obtain news and user representation embedding:1)extracting explicit and latent topic features from news and mining users’preferences for them in historical behaviors;2)extracting entities and propagating users’potential preferences in the knowledge graph.Experiments in a real-world dataset validate the effectiveness and efficiency of our approach.
