Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The gen...Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original images.In this paper,we propose a novel adversarial example generation method,called DCVAE-adv.Different from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images.Furthermore,the proposed method can be applied to both white box and black box attacks.In addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory overhead.We compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack.Our code is available at https://github.com/xzforeverlove/DCVAE-adv.展开更多
2002年,CHOW等人根据数字版权管理(Digital Rights Management,DRM)应用场景定义了白盒攻击环境的概念,并将其模型化为一种极端的攻击模型,即白盒模型。白盒模型颠覆了以往攻击模型中对攻击者能力的诸多限制,从软件保护角度考虑,攻击者...2002年,CHOW等人根据数字版权管理(Digital Rights Management,DRM)应用场景定义了白盒攻击环境的概念,并将其模型化为一种极端的攻击模型,即白盒模型。白盒模型颠覆了以往攻击模型中对攻击者能力的诸多限制,从软件保护角度考虑,攻击者被认为拥有对目标软件及其执行的完全控制权。因此,在白盒模型中,数字版权管理系统中的设备,如智能卡、机顶盒等都存在被攻击者篡改的可能。文章基于CLEFIA算法的白盒实现方案,为数字版权管理系统提供一种软件防篡改方案。该方案将软件的二进制代码文件所解释的查找表隐藏在CLEFIA算法的白盒实现方案的查找表集合中,使软件的防篡改安全性与CLEFIA算法的白盒实现方案的加解密正确性结合在一起。一旦软件发生篡改,CLEFIA算法的白盒实现方案的加解密结果将产生错误。CLEFIA算法白盒实现方案的明密文对也将发生变化,而攻击者很难对其进行修复。展开更多
基金supported by the Key R&D Program of Science and Technology Foundation of Hebei Province(No.19210310D)the Natural Science Foundation of Hebei Province(No.F2021201020).
文摘Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original images.In this paper,we propose a novel adversarial example generation method,called DCVAE-adv.Different from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images.Furthermore,the proposed method can be applied to both white box and black box attacks.In addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory overhead.We compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack.Our code is available at https://github.com/xzforeverlove/DCVAE-adv.
文摘2002年,CHOW等人根据数字版权管理(Digital Rights Management,DRM)应用场景定义了白盒攻击环境的概念,并将其模型化为一种极端的攻击模型,即白盒模型。白盒模型颠覆了以往攻击模型中对攻击者能力的诸多限制,从软件保护角度考虑,攻击者被认为拥有对目标软件及其执行的完全控制权。因此,在白盒模型中,数字版权管理系统中的设备,如智能卡、机顶盒等都存在被攻击者篡改的可能。文章基于CLEFIA算法的白盒实现方案,为数字版权管理系统提供一种软件防篡改方案。该方案将软件的二进制代码文件所解释的查找表隐藏在CLEFIA算法的白盒实现方案的查找表集合中,使软件的防篡改安全性与CLEFIA算法的白盒实现方案的加解密正确性结合在一起。一旦软件发生篡改,CLEFIA算法的白盒实现方案的加解密结果将产生错误。CLEFIA算法白盒实现方案的明密文对也将发生变化,而攻击者很难对其进行修复。
