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Verification of Non-Linear Specifications for Neural Networks

ICLR 2019 · Adversarial robustness and security › Certified and empirical robustness · approach: Neural network robustness verification · 19 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use convex-relaxable specifications and convex optimization to verify nonlinear propertiesNeural network verification · CIFAR-10 semantic consistencyadversarial bound minus verification bound: .9%Helped
Use convex-relaxable specifications and convex optimization to verify nonlinear propertiesNeural network verification · Digit-sum error, N=2adversarial bound minus verification bound: 1.5%Helped
Use the convex relaxation instead of exhaustive verificationPendulum energy verification · Damped pendulum, perturbation radius 0.02verification bound: 73.98% -> 70.00%Hurt
Add an energy-consistency penalty during trainingPendulum dynamics model · Damped pendulum, perturbation radius .06verification bound: 34.53% -> 64.16%Helped
Train for higher verification of semantic consistencyCIFAR-10 classifier · CIFAR-10 semantic specificationverification bound: 84.3% -> 93.5%Helped
Add the SDP constraint to the quadratic relaxation (ablation)Pendulum model B verification · Damped pendulum, small perturbation radius δ≤0.02additional test points verified: 0.9%Helped
Add the SDP constraint to the quadratic relaxation (ablation)Pendulum model B verification · Damped pendulum, increasing perturbation radiusadditional test points verified: 4.8%Helped
Increase the number of summed digits from 2 to 4 (ablation)Digit-sum verification · Digit sum, δ=2/255adversarial bound minus verification bound: 4.2%, ~14.1%, ~26.1%Hurt
Increase the number of summed digits from 2 to 4 (ablation)Digit-sum verification · Digit sum, δ=6/255adversarial bound minus verification bound: 19.9%,~49.7%,~51.8%Hurt
Use a convex-relaxation verifier rather than falsification aloneVerification of nonlinear specifications · Damped pendulum energy specification, perturbation radius 0.06verification bound: 70.00%; 22.05%Mixed

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