{"id":2383,"date":"2018-01-03T06:38:41","date_gmt":"2018-01-03T06:38:41","guid":{"rendered":"https:\/\/www.inforsec.org\/wp\/?p=2383"},"modified":"2018-01-04T06:32:12","modified_gmt":"2018-01-04T06:32:12","slug":"inforsec%e7%bd%91%e7%bb%9c%e5%ae%89%e5%85%a8%e7%a0%94%e7%a9%b6%e5%9b%bd%e9%99%85%e5%ad%a6%e6%9c%af%e8%ae%ba%e5%9d%9b1%e6%9c%8813%e6%97%a5%e6%b8%85%e5%8d%8e%e5%bc%80%e8%ae%b2","status":"publish","type":"post","link":"http:\/\/202.112.238.40\/wp\/?p=2383","title":{"rendered":"InForSec\u7f51\u5b89\u56fd\u9645\u8bba\u575b1\u670813\u65e5\u5b66\u672f\u4e0e\u4ea7\u4e1a\u6e05\u534e\u5bf9\u8bdd"},"content":{"rendered":"
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\u65f6\u95f4\uff1a2018\u5e741\u670813\u65e5\uff08\u5468\u516d\uff098:30-12:00<\/strong><\/p>\n

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\u76f4\u64ad\u5730\u5740\uff1awww.edu.cn\/live<\/a><\/strong><\/p>\n

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8:30-12:00<\/td>\n\u4e3b\u6301\u4eba\uff1a\u6768\u73c9 \u56fd\u5bb6\u9752\u5e74973\u9996\u5e2d\u79d1\u5b66\u5bb6\uff0c\u590d\u65e6\u5927\u5b66\u6559\u6388<\/td>\n<\/tr>\n
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8:45-9:25<\/td>\n\u9648\u6d69<\/td>\n\u7f8e\u56fd\u52a0\u5dde\u5927\u5b66\u6234\u7ef4\u65af\u5206\u6821\u6559\u6388<\/td>\nDefending against Adversarial Examples Based on Manifold and Diversity<\/td>\n<\/tr>\n
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\nTony Lee \u00a0\u4eac\u4e1c\u9996\u5e2d\u5b89\u5168\u4e13\u5bb6
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\u00a0\u6f14\u8bb2\u5609\u5bbe\u53ca\u5185\u5bb9\u6458\u8981<\/strong><\/h3>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

1.\u62a5\u544a\u9898\u76ee\uff1a\u00a0<\/strong>Defending against Adversarial Examples Based on Manifold and Diversity<\/p>\n

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\u6f14\u8bb2\u4eba\uff1a<\/strong>\u9648\u6d69 \u7f8e\u56fd\u52a0\u5dde\u5927\u5b66\u6234\u7ef4\u65af\u5206\u6821\u6559\u6388<\/p>\n

\u5185\u5bb9\u6458\u8981\uff1a<\/strong><\/p>\n

Deep learning systems have shown impressive performance on hard perceptual problems, but they are vulnerable to adversarial examples, which have small, special perturbations that are imperceptible to humans but cause misclassification. Prior defenses either targeted specific attacks or were shown to be ineffective.<\/p>\n

We propose a defense that neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. Our defense is based on two ideas: approximating a manifold to represent normal examples, and using diversity to defeat transfer attacks. To approximate a manifold, we trained an autoencoder based on only normal examples, therefore assuming no specific process for generating adversarial examples. Using the autoencoder, we can detect adversarial examples that are far from the manifold, and draw nearby adversarial examples towards the manifold to increase their classification accuracy. To defeat transfer attacks, we trained a set of diverse autoencoders and randomly selected one of them during testing. Our evaluation shows that this approach is effective against the state of the art attacks. I will also discuss other directions in defense against adversarial examples.<\/p>\n

\u6f14\u8bb2\u4eba\u7b80\u4ecb\uff1a<\/strong><\/p>\n

Hao Chen is a professor at the Department of Computer Science at the University of California, Davis. He received his PhD at the Computer Science Division at the University of California, Berkeley. His current research interests are computer security, machine learning, program analysis and testing, and mobile computing. He won the National Science Foundation CAREER award in 2007, and UC Davis College of Engineering Faculty Award in 2010.<\/p>\n

2.\u62a5\u544a\u9898\u76ee\uff1a<\/strong>\u201c\u6ee4\u7f51\u884c\u52a8\u201d\u6253\u9ed1\u4e24\u5e74\u56de\u987e<\/p>\n

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\u6f14\u8bb2\u4eba\uff1a<\/strong>\u9ec4\u6b63 \u767e\u5ea6\u5b89\u5168\u5b9e\u9a8c\u5ba4X-Team\u8d1f\u8d23\u4eba<\/p>\n

\u5185\u5bb9\u6458\u8981\uff1a<\/strong><\/p>\n

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\u6f14\u8bb2\u4eba\u7b80\u4ecb\uff1a<\/strong><\/p>\n

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3\u3001\u62a5\u544a\u9898\u76ee\uff1a<\/strong>\u57fa\u4e8e\u786c\u4ef6\u8f85\u52a9\u7684\u5185\u6838\u6f0f\u6d1e\u6316\u6398\u6846\u67b6<\/p>\n

\u6f14\u8bb2\u4eba\uff1a<\/strong>\u6f58\u5251\u950b \u00a0360\u9996\u5e2d\u5b89\u5168\u67b6\u6784\u5e08\uff0c\u4e2d\u56fd\u56fd\u5bb6\u4fe1\u606f\u5b89\u5168\u6f0f\u6d1e\u5e93\u9996\u6279\u7279\u8058\u4e13\u5bb6<\/p>\n

\u5185\u5bb9\u6458\u8981\uff1a<\/strong><\/p>\n

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\u6f14\u8bb2\u4eba\u7b80\u4ecb\uff1a<\/strong><\/p>\n

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4\u3001\u6f14\u8bb2\u4e3b\u9898\uff1a<\/strong>\u57fa\u4e8e\u8bed\u4e49\u9a71\u52a8\u53ca\u5b66\u4e60\u5efa\u6a21\u7684\u79fb\u52a8\u5e94\u7528\u9690\u79c1\u6570\u636e\u8bc6\u522b<\/p>\n

Semantics-Driven, Learning-Based Privacy Discovery in Mobile Apps<\/p>\n

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\u5185\u5bb9\u6458\u8981\uff1a<\/strong><\/p>\n

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