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Found 7 papers, 3 papers with code
Boosting Certified Robustness of Deep Networks via a Compositional Architecture
In this work, we propose a new architecture which addresses this challenge and enables one to boost the certified robustness of any state-of-the-art deep network, while controlling the overall accuracy loss, without requiring retraining.
Scalable Polyhedral Verification of Recurrent Neural Networks
We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron.
Adversarial Training and Provable Defenses: Bridging the Gap
We experimentally show that this training method, named convex layerwise adversarial training (COLT), is promising and achieves the best of both worlds -- it produces a state-of-the-art neural network with certified robustness of 60. 5% and accuracy of 78. 4% on the challenging CIFAR-10 dataset with a 2/255 L-infinity perturbation.
Certifying Geometric Robustness of Neural Networks
The use of neural networks in safety-critical computer vision systems calls for their robustness certification against natural geometric transformations (e. g., rotation, scaling).
Certifying Neural Network Audio Classifiers
We present the first end-to-end verifier of audio classifiers.
DL2: Training and Querying Neural Networks with Logic
We present DL2, a system for training and querying neural networks with logical constraints.
