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Since we reduce verification to a search for adversarial examples, we can achieve safety verification or falsification

Safety Verification Of Deep Neural Networks

COMPUTER AIDED VERIFICATION, CAV 2017, PT I, (2017): 3-29

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摘要

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers fo...更多

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简介
  • Deep neural networks have achieved impressive experimental results in image classification, matching the cognitive ability of humans [23] in complex tasks with thousands of classes.
  • Let Rn be a vector space of images that the authors wish to classify and assume that f : Rn → C, where C is a set of class labels, models the human perception capability, a neural network classifier is a function f(x) which approximates f (x) from M training examples {(xi, ci)}i=1,..,M.
  • N}} is a set of activation functions φk : DLk−1 → DLk , one for each non-input layer.
  • Layers other than input and output layers are called the hidden layers
重点内容
  • Deep neural networks have achieved impressive experimental results in image classification, matching the cognitive ability of humans [23] in complex tasks with thousands of classes
  • Since we reduce verification to a search for adversarial examples, we can achieve safety verification or falsification
  • The neural networks are built from a widelyused neural networks library Keras [3] with a deep learning package Theano [6] as its backend
  • We validate DLV on a set of experiments performed for neural networks trained for classification based on a predefined multi-dimensional surface, as well as image classification
  • This paper presents an automated verification framework for checking safety of deep neural networks that is based on a systematic exploration of a region around a data point to search for adversarial manipulations of a given type, and propagating the analysis into deeper layers
  • The network is trained with 5,000 points sampled from the provided two-dimensional space, and has an accuracy of more than 99%
  • The results are encouraging, with adversarial examples found in some cases in a matter of seconds when working with few dimensions, but the verification process itself is exponential in the number of features and has prohibitive complexity for larger images
结果
  • The proposed framework has been implemented as a software tool called DLV (Deep Learning Verification) [2] written in Python, see Appendix of [20] for details of input parameters and how to use the tool.
  • The authors validate DLV on a set of experiments performed for neural networks trained for classification based on a predefined multi-dimensional surface, as well as image classification.
  • These networks respectively use two representative types of layers: fully connected layers and convolutional layers.
  • The first three demonstrate the single-path search functionality on the Euclidean (L2) norm, whereas the fourth (GTSRB) multi-path search for the L1 and L2 norms
结论
  • This paper presents an automated verification framework for checking safety of deep neural networks that is based on a systematic exploration of a region around a data point to search for adversarial manipulations of a given type, and propagating the analysis into deeper layers.
  • It would be interesting to see if the notions of regularity suggested in [24] permit a symbolic approach, and whether an abstraction refinement framework can be formulated to improve the scalability and computational performance
表格
  • Table1: FGSM vs. DLV (on a single path) vs. JSMA
Download tables as Excel
相关工作
  • AI safety is recognised an an important problem, see e.g., [10,33]. An early verification approach for neural networks was proposed in [30], where, using the notation of this paper, safety is defined as the existence, for all inputs in a region η0 ∈ DL0 , of a corresponding output in another region ηn ⊆ DLn . They encode the entire network as a set of constraints, approximating the sigmoid using constraints, which can then be solved by a SAT solver, but their approach only works with 6 neurons (3 hidden neurons). A similar idea is presented in [32]. In contrast, we work layer by layer and obtain much greater scalability. Since the first version of this paper appeared [20], another constraint-based method has been proposed in [21] which improves on [30]. While they consider more general correctness properties than this paper, they can only handle the ReLU activation functions, by extending the Simplex method to work with the piecewise linear ReLU functions that cannot be expressed using linear programming. This necessitates a search tree (instead of a search path as in Simplex), for which a heuristic search is proposed and shown to be complete. The approach is demonstrated on networks with 300 ReLU nodes, but as it encodes the full network it is unclear whether it can be scaled to work with practical deep neural networks: for example, the MNIST network has 630,016 ReLU nodes. They also handle continuous spaces directly without discretisation, the benefits of which are not yet clear, since it is argued in [19] that linear behaviour in high-dimensional spaces is sufficient to cause adversarial examples.
基金
  • For example, This work is supported by the EPSRC Programme Grant on Mobile Autonomy (EP/M019918/1)
研究对象与分析
pairs: 4
a perception module of a self-driving car may input an image from a camera and must correctly classify the type of object in its view, irrespective of aspects such as the angle of its vision and image imperfections. Therefore, though they clearly include imperfections, all four pairs of images in Fig. 1 should arguably be classified as automobiles, since they appear so to a human eye. Classifiers employed in vision tasks are typically multi-layer networks, which propagate the input image through a series of linear and non-linear operators

pairs: 16
We also work with 500 dimensions and otherwise the same experimental parameters as for MNIST. Figure 13 in Appendix of [20] gives 16 pairs of original images (classified correctly) and perturbed images (classified wrongly). We found that, while the manipulations lead to human-recognisable modifications to the images, the perturbed images can be classified wrongly by the network

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