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This article surveys the landscape of security verification approaches and techniques for computer systems at various levels: from a software-application level all the way to the physical hardware level. Different existing projects are compared, based on the tools used and security aspects being examined. Since many systems require both hardware and software components to work together to provide the system’s promised security protections, it is not sufficient to verify just the software levels or just the hardware levels in a mutually exclusive fashion. This survey especially highlights system levels that are verified by the different existing projects and presents to the readers the state of the art in hardware and software system security verification. Few approaches come close to providing full-system verification, and there is still much room for improvement.
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DoppelVer: A Benchmark for Face Verification
The field of automated face verification has become saturated in recent years, with state-of-the-art methods outperforming humans on all benchmarks. Many researchers would say that face verification is close to being a solved problem. We argue that evaluation datasets are not challenging enough, and that there is still significant room for improvement in automated face verification techniques. This paper introduces the DoppelVer dataset, a challenging face verification dataset consisting of doppelganger pairs. Doppelgangers are pairs of individuals that are extremely visually similar, oftentimes mistaken for one another. With this dataset, we introduce two challenging protocols: doppelganger and Visual Similarity from Embeddings (ViSE). The doppelganger protocol utilizes doppelganger pairs as negative verification samples. The ViSE protocol selects negative pairs by isolating image samples that are very close together in a particular embedding space. In order to demonstrate the challenge that the DoppelVer dataset poses, we evaluate a state-of-the-art face verification method on the dataset. Our experiments demonstrate that the DoppelVer dataset is significantly more challenging than its predecessors, indicating that there is still room for improvement in face verification technology.
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- Award ID(s):
- 2150394
- PAR ID:
- 10491771
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- International Symposium on Advances in Visual Computing (ISVC)
- Volume:
- 14361
- ISBN:
- 978-3-031-47969-4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
