DNNF is a tool for applying falsification methods such as adversarial attacks to the checking of DNN correctness problems. Adversarial attacks provide a powerful repertoire of scalable algorithms for property falsification. DNNF leverages these techniques by employing reductions to automatically transform correctness problems into equivalent sets of adversarial robustness problems, to which these attacks can then be applied.
Tools and Artifacts
DNNV is a framework for verifying deep neural networks (DNN). DNN verification takes in a neural network, and a property over that network, and checks whether the property is true, or false. DNNV standardizes the network and property input formats to enable multiple verification tools to run on a single network and property. This facilitates both verifier comparison, and artifact re-use.