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• W2885183727 abstract "Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation." @default.
• W2885183727 created "2018-08-22" @default.
• W2885183727 creator A5004271128 @default.
• W2885183727 creator A5007769527 @default.
• W2885183727 creator A5023463902 @default.
• W2885183727 creator A5047062711 @default.
• W2885183727 creator A5085479490 @default.
• W2885183727 date "2018-07-18" @default.
• W2885183727 modified "2023-09-27" @default.
• W2885183727 title "Motivating the Rules of the Game for Adversarial Example Research" @default.
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• W2885183727 hasPublicationYear "2018" @default.
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