miscellaneous

Security Studies

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in security-studies

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Use these libraries to find security-studies models and implementations

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Most implemented papers

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

A. Mensch, Katie Millican, Roman Ring, Eliza Rutherford, Jacob Menick, Sebastian Borgeaud, Aida Nematzadeh, O. Vinyals, K. Simonyan, Yujia Li, Igor Babuschkin, Po-Sen Huang, Johannes Welbl, K. Kavukcuoglu, D. Hassabis, James Bradbury, Chris Dyer, L. Sifre, George van den Driessche, Erich Elsen, D. Budden, G. Irving, Laura Weidinger, Maribeth Rauh, Iason Gabriel, William S. Isaac, Saffron Huang, Lisa Anne Hendricks, I. Higgins, Diego de Las Casas, Jack W. Rae, Siddhant M. Jayakumar, Xiang Lorraine Li, Francis Song, Cyprien de Masson d'Autume, Amy Wu, Angeliki Lazaridou, A. Kuncoro, Michela Paganini, Trevor Cai, Jordan Hoffmann, John Aslanides, Sarah Henderson, Susannah Young, Tom Hennigan, Albin Cassirer, Richard Powell, Amelia Glaese, Sumanth Dathathri, Jonathan Uesato, John F. J. Mellor, Antonia Creswell, Nat McAleese, Elena Buchatskaya, Esme Sutherland, Lena Martens, E. Gribovskaya, Domenic Donato, Jean-Baptiste Lespiau, M. Tsimpoukelli, N. Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Tobias Pohlen, Z. Gong, Daniel Toyama, Tayfun Terzi, Vladimir Mikulik, Aidan Clark, Aurelia Guy, Chris Jones, Matthew G. Johnson, Blake A. Hechtman, Edward Lockhart, Simon Osindero, Laura Rimell, Kareem W. Ayoub, J. Stanway, L. Bennett

Content

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Tue Dec 07 2021

This paper presents an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.

1527 0

Paper Graph

Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings

D. Zakharov, Oleksandr Kuznetsov, Emanuele Frontoni•Thu Jan 25 2024

An innovative image transformation technique is introduced that effectively renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models, which allows the distorted photo version to be stored for further verification.

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Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries

G. Ateniese, Dario Pasquini, Marco Cianfriglia, M. Bernaschi•Thu Oct 22 2020

This work introduces new guessing techniques that make dictionary attacks consistently more resilient to inadequate configurations, and introduces automatic dynamic strategies within dictionary attacks to mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets.

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3D Fully Convolutional Neural Networks with Intersection over Union Loss for Crop Mapping from Multi-Temporal Satellite Images

Sina Mohammadi, Mariana Belgiu, A. Stein•Sun Feb 14 2021

The capability of a 3D Fully Convolutional Neural Network to map crop types from multi-temporal images and the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data are explored.

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Embedding Non-Distortive Cancelable Face Template Generation

D. Zakharov, Oleksandr Kuznetsov, Emanuele Frontoni, Natalia Kryvinska•Sat Feb 03 2024

An innovative image distortion technique is introduced that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model, and is tested by determining the maximum image distortion that does not change the predicted identity.

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