secml
@secml_py
secml: Secure and Explainable Machine Learning in Python Code: http://gitlab.com/secml/secml Docs: http://secml.gitlab.io Paper: http://arxiv.org/abs/1912.10013
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secml v0.14 is out! gitlab.com/secml/secml github.com/pralab/secml secml.gitlab.io Highlights: - Foolbox attacks and RobustBench models now included, with notebooks - New notebook tutorial with an application on Android Malware Detection
Update SecML Malware 0.2.6: support the SOREL DNN! Thank you @rharang again for releasing it! github.com/pralab/secml_m… #secml #advml #malware #security #research
Now part of FoolBox too!
📢Very happy to announce that our paper "Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints" was accepted for a poster presentation at @NeurIPSConf! This is joint work with @wielandbr, @biggiobattista, and Fabio Roli. 1/n
Join us for our event on Machine Learning Security! Tuesday, March 8th, 2022, at 16:00 CET. Invited talk by Francesco Croce (University of Tübingen). Registration: eventbrite.com/e/machine-lear… YT Live: youtu.be/MrRPTB0ZmJw #adversarial #machinelearning #ai #security #mlsec
youtube.com
YouTube
Machine Learning Security Seminar Series - Francesco Croce
Join us for our first seminar event on Machine Learning Security Tomorrow, Dec 7th, 2021, at 15.00 CET 🥳 Invited talk by David Stutz (Max Planck Institute for Informatics). Registration here (free): eventbrite.it/e/machine-lear… YT Live Stream: youtu.be/hJngoKRriO8
youtube.com
YouTube
Machine Learning Security Seminar Series - David Stutz
We are excited to present our seminar series on Adversarial Machine Learning! We will host David Stutz (Max Planck Institute) for our first event on Dec 7th at 15:00 CET. Free registration here: eventbrite.it/e/biglietti-ma… #adversarial #machinelearning #ai #security
SecML + docker + GUI... it's PandaVision! Cool work by @maurapintor
Dream: evaluating adversarial robustness (with attack configuration!) without writing code Reality: github.com/maurapintor/pa… Check out my recent work, 🌟 the repo if you like it! Preview: Integrate and automate security evaluations with ONNX, PyTorch, and SecML! Video in 🧵!
github.com
GitHub - maurapintor/pandavision: Security evaluation module with onnx, pytorch, and SecML.
Security evaluation module with onnx, pytorch, and SecML. - maurapintor/pandavision
The second lecture of the course "Machine Learning Security" is on YouTube: youtu.be/hC1l4MaykzU Topic: Adversarial Examples and defenses. Lecturer: @biggiobattista, @zangobot #MLSec #MachineLearning #AI #adversarial #Malware @adversarial_ML @trustworthy_ml @aivillage_dc
We've just published the first lesson of the short course "Machine Learning Security" on YouTube: youtu.be/5wOWcWepktM github.com/unica-mlsec/ml… #MLsec #MachineLearning #AI #adversarial #malware @adversarial_ML @trustworthy_ml @aivillage_dc
We're preparing a short course for PhD students on machine learning security, and open sourcing the content. Any feedback is more than welcome -- towards improving next year's extended edition! github.com/unica-mlsec/ml…
Refactoring is ongoing. Fasten your seatbelts as we'll have fun soon! And star our github repo if you like secml! github.com/pralab/secml
Our article for the @AssureMOSSH2020 WP3, dedicated to continuos analysis and correction of secure code @secml_py (Secure ML Library) is an #opensource #Python library for the security evaluation of #MachineLearning algorithms #cybersecurity #ai #ml @MarcoMelisIT @ambrademontis
SecML, a library for Secure and Explainable Machine Learning by SecML, a library for Secure and Explainable Machine Learning by @pluribus_one (@Matteo_Mauri_, @biggiobattista ) For the full article: assuremoss.eu/en/news/SecML-…
ALOHA defines a framework for optimizing the design of Deep Learning systems on heterogeneous low-energy computing platforms, and includes adversarial robustness evaluation with @secml_py . Check out the workshop where we show the achievements of this project!
The ALOHA Final results will be presented virtually at: rebrand.ly/aloha-final-wo… ALOHA enables #deeplearning in new applications and automates the implementation of DL inference on low-power #embeddedsystems #neuralnetworks #ai #machinelearning More info: tinyurl.com/3wxyrka2
Find efficiently minimum-norm Adversarial Examples in different L-p norms with FMN! 🙌 Updated (increasing) list of available implementations: paperswithcode.com/paper/fast-min… @biggiobattista @wielandbr
📍Paper updated: Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Paper: arxiv.org/pdf/2102.12827… Github: github.com/pralab/Fast-Mi… Available within @secml_py and Foolbox. w/ @maurapintor @wielandbr
While #machinelearning applications can be exposed to common security threats, they are also exposed to domain specific threats that are often overlooked. #ProjectBlackfin took a look at one of the most serious threats #AI faces -- data poisoning. blog.f-secure.com/data-poisoning…
Our model zoo is growing!
This week we added new #pretrained #models for @secml in our zoo: - Support Vector Machines and our Secure SVM from arxiv.org/abs/1704.08996, on the Drebin #android ds - #pytorch AlexNet + SVMs from arxiv.org/abs/1708.06939, on the #iCub ds Check them out: github.com/pralab/secml-z…
Despite current difficult times, work on our projects continues!
secml v0.14 is out! gitlab.com/secml/secml github.com/pralab/secml secml.gitlab.io Highlights: - Foolbox attacks and RobustBench models now included, with notebooks - New notebook tutorial with an application on Android Malware Detection
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Machine Learning Security Laboratory
@mlsec_lab -
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@biggiobattista -
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@g_apru -
Fabio Pierazzi
@fbpierazzi -
Ambra
@ambrademontis -
Ram Shankar Siva Kumar
@ram_ssk -
Luca Demetrio
@zangobot -
EUGENE NEELOU
@eneelou -
Konrad Rieck 🌈
@mlsec -
francesco croce
@fra__31 -
Ilia Shumailov🦔
@iliaishacked -
Lorenzo
@LorenzoCazz -
Lorenzo Cavallaro
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Angelo Sotgiu
@sotgiu_angelo -
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