This week we added new #pretrained#models for @secml in our zoo:
- Support Vector Machines and our Secure SVM from https://t.co/QhsXBZGGvy, on the Drebin #android ds
- #pytorch AlexNet + SVMs from https://t.co/ogQyXLJFgQ, on the #iCub ds
Check them out: https://t.co/7hXBYcFNim
secml v0.14 is out!
https://t.co/j6MkKuk2IT
https://t.co/CnEDnkOjzj
https://t.co/UAizk3bEOb
Highlights:
- Foolbox attacks and RobustBench models now included, with notebooks
- New notebook tutorial with an application on Android Malware Detection
Our new paper “FADER: Fast Adversarial Example Detection” is out! Check it if you want to speed up adversarial examples detection! https://t.co/EUN3d60Dz9
@biggiobattista@MarcoMelisIT@sotgiu_angelo
New notebook on adversarial Android malware attacks by @MarcoMelisIT!
https://t.co/E0eB4egygp
Paper (w/ attack details): https://t.co/Pg7CuNBW77
With secml we can craft sparse attacks that only inject Boolean features - and explain the classification results.
Check it out!
v0.13 is out!
https://t.co/3yPY7H8cEK
with major improvements and the implementation of deep neural rejection (DNR) to counter adversarial examples!
Notebook: https://t.co/aEyGMZLedf
Paper: https://t.co/SMhwH6KYoM
New hotfix v0.11.2 for #SecML addressing a few issues with attack and optimization classes.
Also, check the new tutorial on advanced attacks using #imagenet and #neuralnetworks from our paper:
https://t.co/ThR6S28Ao6
https://t.co/HZzfdacQ6t
Changelog: https://t.co/APbPN6Ji3T
We just released a hotfix for #SecML addressing compatibility issues with recently released #sklearn v0.22 and #scipy v1.4.
Changelog: https://t.co/APbPN6rGFj
We just released SecML v0.11, including the new #modelzoo, multiple performance optimizations, and general improvements.
Also check the new tutorial on the use of #cleverhans within SecML: https://t.co/oEdqSTgfAa
Code: https://t.co/77OqeXwJiV
Changelog: https://t.co/APbPN6Ji3T
Check out the now available SecML v0.10 with #explainableML and #cleverhans library connector (beta). In addition, multiple enhancements to #pytorch support.
New tutorials available here: https://t.co/oEdqSTgfAa
Code: https://t.co/77OqeXwJiV
Changelog: https://t.co/APbPN6Ji3T
The new v0.9 update for SecML is now available, which adds support to #NeuralNetworks through #PyTorch.
Many thanks for the positive feedback on the project so far.
Have a look at the new tutorials available here: https://t.co/HD0zePklPi
Code: https://t.co/77OqeXwJiV
We just released a small update (v0.8.1) for SecML with a fix for documentation not building correctly. Thanks for the feedback.
To send us suggestions and bug reports please use the issue tracker: https://t.co/qXQTGbqYsv
Docs: https://t.co/oEdqSTgfAa
We've finally released a beta version of secML, with poisoning and evasion attacks (also on sparse data). Notebooks are available.
Support for DL and other advML libraries will be added soon.
Feedback is welcome!
Code: https://t.co/OuUnVLeqzl
Docs: https://t.co/LoYCJSfzQ7
Our recent paper at #EUSIPCO2018 showing that #ML algorithms overemphasise few features trying to detect #Android#malware, which explains why they are vulnerable and why attacks are transferable across different models. #interpretableML#explainableAI
https://t.co/jiJcyyTwmS