深度网络中的学习意义及对抗性攻击的检测

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3t7Z3duvHBw+6SRTDH2WiES1QqpRcIm+4UZgK1VI41BgMxzeTv3mCJXmiXw04xSDmPYljzijxkoPo67brVTdmjsDWSZeQapQoNGtfHV6CctilIYJqnXbc1MT5FQZzgROyp1MY0rZkPaxbamkMeogn506IadW6ZEoUbakITP190ROY63HcWg7Y2oGetGbiv957cxEV0HOZZoZlGy+KMoEMQmZ/k16XCEzYmwJZYrbWwkbUEWZsemUbQje4svLxD+vXdfc+4tq/aZIowTHcAJn4MEl1OEOGuADgz48wyu8OcJ5cd6dj3nrilPMHMEfOJ8/dJaNcA==FUNCTIO 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CAVITIESFUNCTIONAL BINARY 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AAAB73icbVDLSgNBEOyNrxhfUY9eBoPgKeyKoN6CXjxGcE0kCWF2MpsMmccyMyuEJV/hxYOKV3/Hm3/jbLIHTSxoKKq66e6KEs6M9f1vr7Syura+Ud6sbG3v7O5V9w8ejEo1oSFRXOl2hA3lTNLQMstpO9EUi4jTVjS+yf3WE9WGKXlvJwntCTyULGYEWyc9dvVI9bPxtNKv1vy6PwNaJkFBalCg2a9+dQeKpIJKSzg2phP4ie1lWFtGOJ1WuqmhCSZjPKQdRyUW1PSy2cFTdOKUAYqVdiUtmqm/JzIsjJmIyHUKbEdm0cvF/7xOauPLXsZkkloqyXxRnHJkFcq/RwOmKbF84ggmmrlbERlhjYl1GeUhBIsvL5PwrH5V9+/Oa43rIo0yHMExnEIAF9CAW2hCCAQEPMMrvHnae/HevY95a8krZg7hD7zPHwk7kBg=INPUTDNNANALYSIS147570深度网络中的学习意味着什么？如何检测对抗性攻击？0Ciprian A. Corneanu Univ. Barcelona Meysam Madadi CVC, UAB Sergio Escalera Univ.Barcelona Aleix M. Martinez OSU0摘要0深度神经网络（DNNs）的灵活性和高准确性已经改变了计算机视觉。但是，我们不知道特定的DNN何时工作，何时失败，导致了缺乏信任。一个明显的例子是自动驾驶汽车；人们不舒服坐在由可能在某些未知、不可预测的条件下失败的算法驱动的汽车中。可解释性和可解释性方法试图通过揭示DNN模型的内容来解决这个问题，即网络中的每个节点（单元）代表什么，最可能激活它的图像是什么。这可以用来生成对抗性攻击。但是，这些方法通常不能确定DNN在哪里成功或失败以及为什么。即，这种学习表示是否泛化到未见样本？在这里，我们提出了一种新的方法来定义深度网络中的学习意义，以及如何利用这个知识来检测对抗性攻击。我们展示了这如何定义网络对未见测试样本的泛化能力，以及最重要的是，为什么会这样。01. 引言0深度神经网络（DNNs）具有足够的容量，可以从非常大的数据集中学习，无需为每个问题定义手工特征、模型或假设。这在计算机视觉和其他人工智能领域引起了革命[15]。然而，使用高容量的DNNs从大型数据集中学习并不能告诉我们网络是如何学习的以及为什么学习。这个所谓的“黑盒子”问题导致了缺乏信任[5,35]。例如，为什么一个给定的DNN将一个特定的深度表示识别为比其他可能的表示更合适？可解释性和可解释性方法[34]允许我们看到DNN学到了什么，但不知道它是如何学到的以及为什么学到的。例如，如果目标是知道什么类型的图像会激活网络的特定节点（单元），可以优化DeepDream或其他相关标准[1]。当我们的目标是生成合理的类输出图像时，可以执行优化softmax和相关目标[27]。但是，这两种方法都不能告诉我们这些深度特征（表示）是如何被网络选择的，以及它们可能失败的位置。缺乏可解释性和可解释性的主要问题是：1.如果我们不知道DNNs是如何学习的，我们只能通过试错来改进它们。这是缓慢的，不能解决信任问题。而且，2.如果我们不知道DNNs为什么学习了一个特定的深度表示或模型，我们将不知道网络在哪里和为什么失败，例如，检测对抗性攻击。借助代数拓扑学的帮助，我们推导出一组算法，帮助我们解决上述问题。网络的简单局部属性（例如节点的度）以及全局属性（例如节点之间的路径长度）被发现无法解释DNNs是如何学习的[3]。本文展示了功能性网络的拓扑属性（例如n-团和nD孔）如何描述DNNs的学习过程，图1。我们使用这些属性来证明我们可以预测DNN何时成功学习，以及何时以及为什么它可能对未见的测试样本进行错误分类（算法1和算法2）。我们还展示了如何使用推导出的方法成功检测对抗性攻击。0v 50v 20v 30v 40v 10v 00β 10β 20β 30ρ 10ρ k01. DNN是否学习？2.DNN是否泛化？3.对抗样本？0图1.给定一个DNN，其功能图由远程节点的激活相关性定义。这使我们能够计算定义网络的局部和全局拓扑属性的二进制图（算法1）。全局拓扑属性定义了泛化能力，而局部属性则识别过拟合（算法2）。相同的拓扑属性用于检测对抗性攻击。0优化softmax和相关目标可能会执行[27]。但是，这两种方法都不能告诉我们这些深度特征（表示）是如何被网络选择的，以及它们可能失败的位置。缺乏可解释性和可解释性的主要问题是：1.如果我们不知道DNNs是如何学习的，我们只能通过试错来改进它们。这是缓慢的，不能解决信任问题。而且，2.如果我们不知道DNNs为什么学习了一个特定的深度表示或模型，我们将不知道网络在哪里和为什么失败，例如，检测对抗性攻击。借助代数拓扑学的帮助，我们推导出一组算法，帮助我们解决上述问题。网络的简单局部属性（例如节点的度）以及全局属性（例如节点之间的路径长度）被发现无法解释DNNs是如何学习的[3]。本文展示了功能性网络的拓扑属性（例如n-团和nD孔）如何描述DNNs的学习过程，图1。我们使用这些属性来证明我们可以预测DNN何时成功学习，以及何时以及为什么它可能对未见的测试样本进行错误分类（算法1和算法2）。我们还展示了如何使用推导出的方法成功检测对抗性攻击。v2AB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKoN6KXjxWNLbQhrLZbtqlm03YnRK6U/w4kHFq/Im/GbZuDtj4YeLw3w8y8MJXCoOt+O4W19Y3NreJ2aWd3b/+gfHj0ZJM+6zRCa6FVLDpVDcR4GSt1LNaRxK3gyHtzO/OeLaiEQ94jlQUz7SkSCUbTSw6hb65YrbtWdg6wSLycVyNHolr86vYRlMVfIJDWm7bkpBhOqUTDJp6VOZnhK2ZD2edtSRWNugsn81Ck5s0qPRIm2pZDM1d8TExobM45D2xlTHJhlbyb+57UzjK6CiVBphlyxaIokwQTMvub9ITmDOXYEsq0sLcSNqCaMrTplGwI3vLq8SvVa+r7v1FpX6Tp1GEziFc/DgEupwBw3wgUEfnuEV3hzpvDjvzseiteDkM8fwB87nD3ecjXI=AB6XicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKoN6KXjxWNLbQhrLZbtqlm03YnRK6U/w4kHFq/Im/GbZuDtj4YeLw3w8y8MJXCoOt+O4W19Y3NreJ2aWd3b/+gfHj0ZJM+6zRCa6FVLDpVDcR4GSt1LNaRxK3gyHtzO/OeLaiEQ94jlQUz7SkSCUbTSw6hb65YrbtWdg6wSLycVyNHolr86vYRlMVfIJDWm7bkpBhOqUTDJp6VOZnhK2ZD2edtSRWNugsn81Ck5s0qPRIm2pZDM1d8TExobM45D2xlTHJhlbyb+57UzjK6CiVBphlyxaIokwQTMvub9ITmDOXYEsq0sLcSNqCaMrTplGwI3vLq8SvVa+r7v1FpX6Tp1GEziFc/DgEupwBw3wgUEfnuEV3hzpvDjvzseiteDkM8fwB87nD3ecjXI=v1AB6XicbVBNS8NAEJ34WetX1aOXxSJ4KokI6q3oxWNFYwtKJvtpF262YTdTaGE/gQvHlS8+o+8+W/ctjlo64OBx3szMwLU8G1cd1vZ2V1bX1js7RV3t7Z3duvHBw+6SRTDH2WiES1QqpRcIm+4UZgK1VI41BgMxzeTv3mCJXmiXw04xSDmPYljzijxkoPo67XrVTdmjsDWSZeQapQoNGtfHV6CctilIYJqnXbc1MT5FQZzgROyp1MY0rZkPaxbamkMeogn506IadW6ZEoUbakITP190ROY63HcWg7Y2oGetGbiv957cxEV0HOZoZlGy+KMoEMQmZ/k16XCEzYmwJZYrbWwkbUEWZsemUbQje4svLxD+vXdfc+4tq/aZIowTHcAJn4MEl1OEOGuADgz48wyu8OcJ5cd6dj3nrilPMHMEfOJ8/dhmNcQ=AB6XicbVBNS8NAEJ34WetX1aOXxSJ4KokI6q3oxWNFYwtKJvtpF262YTdTaG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