4 research outputs found
symQV: Automated Symbolic Verification of Quantum Programs
We present symQV, a symbolic execution framework for writing and verifying
quantum computations in the quantum circuit model. symQV can automatically
verify that a quantum program complies with a first-order specification. We
formally introduce a symbolic quantum program model. This allows to encode the
verification problem in an SMT formula, which can then be checked with a
delta-complete decision procedure. We also propose an abstraction technique to
speed up the verification process. Experimental results show that the
abstraction improves symQV's scalability by an order of magnitude to quantum
programs with 24 qubits (a 2^24-dimensional state space).Comment: This is the extended version of a paper with the same title that
appeared at FM 2023. Tool available at doi.org/10.5281/zenodo.740032
SpecAttack: Specification-Based Adversarial Training for Deep Neural Networks
Safety specification-based adversarial training aims to generate examples
violating a formal safety specification and therefore provides approaches for
repair. The need for maintaining high prediction accuracy while ensuring the
save behavior remains challenging. Thus we present SpecAttack, a
query-efficient counter-example generation and repair method for deep neural
networks. Using SpecAttack allows specifying safety constraints on the model to
find inputs that violate these constraints. These violations are then used to
repair the neural network via re-training such that it becomes provably safe.
We evaluate SpecAttack's performance on the task of counter-example generation
and repair. Our experimental evaluation demonstrates that SpecAttack is in most
cases more query-efficient than comparable attacks, yields counter-examples of
higher quality, with its repair technique being more efficient, maintaining
higher functional correctness, and provably guaranteeing safety specification
compliance
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application toWebscale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA’s performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks.
Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importance to certify the safety of these DNNs, i.e. that they comply with a formal safety specification. While safety certification tools exactly answer this question, they are of no help in debugging unsafe DNNs, requiring the developer to iteratively verify and modify the DNN until safety is eventually achieved. Hence, a repair technique needs to be developed that can produce a safe DNN automatically. To address this need, we present SpecRepair, a tool that efficiently eliminates counter-examples from a DNN and produces a provably safe DNN without harming its classification accuracy. SpecRepair combines specification-based counter-example search and resumes training of the DNN, penalizing counter-examples and certifying the resulting DNN. We evaluate SpecRepair’s effectiveness on the ACAS Xu benchmark, a DNN-based controller for unmanned aircraft, and two image classification benchmarks. The results show that SpecRepair is more successful in producing safe DNNs than comparable methods, has a shorter runtime, and produces safe DNNs while preserving their classification accuracy.publishe