@ak1010.bsky.social
Reposted
Breaking NATO Radio Encryption: Cryptanalysis of HALFLOOP Block Ciphers media.ccc.de/v/38c3-break... < 38C3 talk by Lukas Stennes
Two papers:
1. Breaking HALFLOOP-24 (Dansarie et al., 2022) doi.org/10.46586/tos...
2. Destroying HALFLOOP-24 (Leander et al., 2023) doi.org/10.46586/tos...
Two papers:
1. Breaking HALFLOOP-24 (Dansarie et al., 2022) doi.org/10.46586/tos...
2. Destroying HALFLOOP-24 (Leander et al., 2023) doi.org/10.46586/tos...
Breaking NATO Radio Encryption
We present fatal security flaws in the HALFLOOP-24 encryption algorithm, which is used by the US military and NATO. HALFLOOP-24 was meant...
media.ccc.de
December 29, 2024 at 4:44 PM
Breaking NATO Radio Encryption: Cryptanalysis of HALFLOOP Block Ciphers media.ccc.de/v/38c3-break... < 38C3 talk by Lukas Stennes
Two papers:
1. Breaking HALFLOOP-24 (Dansarie et al., 2022) doi.org/10.46586/tos...
2. Destroying HALFLOOP-24 (Leander et al., 2023) doi.org/10.46586/tos...
Two papers:
1. Breaking HALFLOOP-24 (Dansarie et al., 2022) doi.org/10.46586/tos...
2. Destroying HALFLOOP-24 (Leander et al., 2023) doi.org/10.46586/tos...
Reposted
*AI for Mathematics and Theoretical Computer Science*, a workshop hosted jointly by the Simons Institute for the Theory of Computing and the Simons Laufer Mathematical Sciences Institute, will be held in Berkeley at April 7-11, 2025.
simons.berkeley.edu/workshops/si...
simons.berkeley.edu/workshops/si...
Simons Institute for the Theory of Computing and SLMath Joint Workshop: AI for Mathematics and Theoretical Computer Science
This is an exciting time for mathematics, as new technologies for mathematical reasoning provide novel opportunities for mathematical research, communication, and discovery. Mathlib, a library of form...
simons.berkeley.edu
December 2, 2024 at 10:08 PM
*AI for Mathematics and Theoretical Computer Science*, a workshop hosted jointly by the Simons Institute for the Theory of Computing and the Simons Laufer Mathematical Sciences Institute, will be held in Berkeley at April 7-11, 2025.
simons.berkeley.edu/workshops/si...
simons.berkeley.edu/workshops/si...
Reposted
When we enter a new environment, we use visual input to rapidly build an internal model of the local spatial environment. How does our brain do this? We review past literature and suggest some new ways forward in our new review in @currentbiology.bsky.social: authors.elsevier.com/sd/article/S...
ScienceDirect.com | Science, health and medical journals, full text articles and books.
authors.elsevier.com
December 2, 2024 at 5:38 PM
When we enter a new environment, we use visual input to rapidly build an internal model of the local spatial environment. How does our brain do this? We review past literature and suggest some new ways forward in our new review in @currentbiology.bsky.social: authors.elsevier.com/sd/article/S...
Reposted
The ZenML team has compiled over 300 real-world examples of deploying LLMs in production. It might help technical teams tackle practical challenges with a focus on concrete implementation details while avoiding promotional fluff www.zenml.io/blog/demysti... #dataBS
Demystifying LLMOps: A Practical Database of Real-World Generative AI Implementations - ZenML Blog
The LLMOps Database offers a curated collection of 300+ real-world generative AI implementations, providing technical teams with practical insights into successful LLM deployments. This searchable res...
www.zenml.io
December 2, 2024 at 1:58 PM
The ZenML team has compiled over 300 real-world examples of deploying LLMs in production. It might help technical teams tackle practical challenges with a focus on concrete implementation details while avoiding promotional fluff www.zenml.io/blog/demysti... #dataBS
Reposted
Ensemble Everything Everywhere is a defense against adversarial examples that people got quite exited about a few months ago (in particular, the defense causes "perceptually aligned" gradients just like adversarial training)
Unfortunately, we show it's not robust...
arxiv.org/abs/2411.14834
Unfortunately, we show it's not robust...
arxiv.org/abs/2411.14834
Gradient Masking All-at-Once: Ensemble Everything Everywhere Is Not Robust
Ensemble everything everywhere is a defense to adversarial examples that was recently proposed to make image classifiers robust. This defense works by ensembling a model's intermediate representations...
arxiv.org
November 25, 2024 at 8:38 AM
Ensemble Everything Everywhere is a defense against adversarial examples that people got quite exited about a few months ago (in particular, the defense causes "perceptually aligned" gradients just like adversarial training)
Unfortunately, we show it's not robust...
arxiv.org/abs/2411.14834
Unfortunately, we show it's not robust...
arxiv.org/abs/2411.14834