Neural Network to Forge Fingerprints

DeepMasterPrints can be used as a key for hacking biometric identification systems
16 November 2018   294

Researchers at New York University have developed a generative-adversary network for prototyping fingerprints. Images of these prints, called DeepMasterPrints, can be used as a key for hacking biometric identification systems.

The principle of creating DeepMasterPrints is to use two properties of fingerprints and biometric systems.

Many scanners do not read the entire print. They process a part of it, and then compare it with the exact same part of the print from the database. Thus, a fake print should correspond only to a part of the original one.

Secondly, the researchers noted that some features of prints are repeated. This means that an artificial footprint that contains a set of common features will correspond to several genuine footprints at once.

DeepMasterPrints
DeepMasterPrints

The study showed that at a system tolerance level of 0.1%, artificial fingerprints can forge up to 23% of all fingerprints from the database. An error of 1% allows neural networks to fake up to 77% of prints.

Experts compare the use of DeepMasterPrints with a brute-force attack, when an attacker sorts through all the passwords that may be appropriate in this case.

Facebook to Release PyTorch 1.0

This release added support for large cloud platforms, a C ++ interface, a set of JIT compilers
10 December 2018   125

Facebook has released a stable version of the library for machine learning PyTorch 1.0. This iteration added support for large cloud platforms, a C ++ interface, a set of JIT compilers, and various improvements.

The stable version received a set of JIT compilers that eliminate the dependence of the code on the Python interpreter. The model code is transformed into Torch Script - a superstructure over Python. Keeping the opportunity to work with the model in the Python environment, the user can download it to other projects not related to this language. So, the PyTorch developers state that the code processed in this way can be used in the C ++ API.

The torch.distributed package and the torch.nn.parallel.DistributedDataParallel module are completely redesigned. torch.distributed now has better performance and works asynchronously with the Gloo, NCCL and MPI libraries.

The developers added a C ++ wrapper to PyTorch 1.0. It contains analogs of Python interface components, such astorch.nn,torch.optim, torch.data. According to the creators, the new interface should provide high performance for C ++ applications. True, the C ++ API is still experimental, but it can be used in projects now.

To improve the efficiency of working with PyTorch 1.0, a Torch Hub repository has been created, which stores pre-trained models of neural networks. You can publish your own development using the hubconf.py file, after which the model will be available for download by any user via the torch.hub.load API.

Support for C extensions and the module torch.utils.trainer were removed from the library.

Facebook released the preliminary version of PyTorch 1.0 at the beginning of October 2018, and in two months the developers brought the framework to a stable state.