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Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance

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Academic year: 2020

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Figure

Figure 1: (Left) Convergence of lower bound, upper bound, and estimation of dm for oneimage; (Middle) That of global robustness; (Right) Boxplots of the computational time.
Figure 5 (note in log-scale) gives running times. Our tensor-based parallelisation method delivers extremely efficient at-1050tacks
Table 1: Neuron coverage achieved by DeepTRE and other tools
Figure 8: Lower bounds, upper boundsand estimates of local robustness for 5ImageNet DNNs on a given image.

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