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2852results about How to "Avoid attack" patented technology

Digital Content Protection Method and Apparatus

<heading lvl="0">Abstract of Disclosure</heading> Before use, a population of tamper-resistant cryptographic enforcement devices is partitioned into groups and issued one or more group keys. Each tamper-resistant device contains multiple computational units to control access to digital content. One of the computational units within each tamper-resistant device communicates with another of the computational units acting as an interface control processor, and serves to protect the contents of a nonvolatile memory from unauthorized access or modification by other portions of the tamper-resistant device, while performing cryptographic computations using the memory contents. Content providers enforce viewing privileges by transmitting encrypted rights keys to a large number of recipient devices. These recipient devices process received messages using the protected processing environment and memory space of the secure unit. The processing result depends on whether the recipient device was specified by the content provider as authorized to view some encrypted digital content. Authorized recipient devices can use the processing result in decrypting the content, while unauthorized devices cannot decrypt the content. A related aspect of the invention provides for securing computational units and controlling attacks. For example, updates to the nonvolatile memory, including program updates, are supported and protected via a cryptographic unlocking and validation process in the secure unit, which can include digital signature verification.
Owner:CRYPTOGRAPHY RESEARCH

Authentication system based on biological characteristics and identification authentication method thereof

The invention relates to an authentication system and an authentication method based on biological features. The method comprises the steps of conducting centralized storage and biological feature comparison operation for a large quantity of user biological feature templates according to user ID identifiers stored on a memory on a limited use machine and corresponding biding ID identifiers thereof on a biological authentication device of a digital processing terminal; separating the biological feature templates from the authentication function of the limited use machine; allocating to digital processing terminals (including PCs, notebooks or mobile phones, PDAs of users)of users as biological authentication device-storing the biological feature templates with decentralization and operating and processing biological features to be authenticated with decentralization so as to conduct comparison with the biological feature templates pre-stored on the respective digital processing terminals; feeding back comparison and verification results to the limited use machines; authorizing to conduct equal authentication of the biological authentication device ID identifier on the digital processing terminal for the corresponding binding user ID identifiers by the centralized authentication of the limited use machine according to the comparison verification result data.
Owner:刘洪利

Multi-model cooperative defense method facing deep learning antagonism attack

A multi-model cooperative defense method facing deep learning antagonism attack comprises the following steps of: 1) performing unified modeling based on a gradient attack to provide a [Rho]-loss model; 2) according to design of a unified model, for an countering attack of a target model fpre(x), according to a generation result of a countering sample, classifying a basic expression form of an attack into two classes; 3) analyzing the parameters of the model, performing parameter optimization of the [Rho]-loss model and search step length optimization of a disturbance solution model for the countering sample; and 4) for the mystique of a black box attack, designing an experiment based on an adaboost concept, generating a plurality of different types of substitution models, used to achievethe same task, for integration, designing a multi-model cooperative defense method with high defense capability through an attack training generator of an integration model with high defense capability, and providing multi-model cooperative detection attack with weight optimal distribution. The multi-model cooperative defense method is high in safety and can effectively defense the attack of a deep learning model for the antagonism attack.
Owner:ZHEJIANG UNIV OF TECH
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