The ultimate goal of verification is to guarantee the safety of deployed neural networks. Here, we claim that all the state-of-the-art verifiers we are aware of fail to reach this goal. Our key insight is that theoretical soundness (bounding the full-precision output while computing with floating point) does not imply practical soundness (bounding the floating point output in a potentially stochastic environment). We prove this observation for the approaches that are currently used to achieve provable theoretical soundness, such as interval analysis and its variants. We also argue that achieving practical soundness is significantly harder computationally. We support our claims empirically as well by evaluating several well-known verification methods. To mislead the verifiers, we create adversarial networks that detect and exploit features of the deployment environment, such as the order and precision of floating point operations. We demonstrate that all the tested verifiers are vulnerable to our new deployment-specific attacks, which proves that they are not practically sound.

Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance than any of the original heuristics. While HHs lend themselves naturally for distributed deployment, relatively little attention has been paid so far on the design and evaluation of distributed HHs. To our knowledge, our work is the first to present a detailed evaluation and comparison of distributed HHs for real parameter optimization in an island model. Our set of test functions includes well-known benchmark functions and two realistic space-probe trajectory optimization problems. The set of algorithms available to the HHs include several variants of differential evolution, and uniform random search. Our main conclusion is that some of the simplest HHs are surprisingly successful in a distributed environment, and the best HHs we tested provide a robust and stable good performance over a wide range of scenarios and parameters.