Angelo Sotgiu

[Angelo Sotgiu @sotgiu_angelo]

🆕 There's still time to submit your work! The deadline has been extended to October 17, 2025 (EDT)!

[Angelo Sotgiu @sotgiu_angelo]

📢 Check out the S2RAI Track (at SAC26 Conference)!

I am co-chairing with Simon Burton, Marta Catillo, Jeremie Guiochet, and Tommaso Zoppi

🗓️ Deadline: September 26th, 2025

ℹ️ Info here: https://t.co/vihwOBoCnq

🇬🇷 SAC26 Website: https://t.co/4ApQoVQrbT

[Angelo Sotgiu @sotgiu_angelo]

🆕 UPDATE: the deadline has been extended to October 10, 2025 (EDT)!

[Angelo Sotgiu @sotgiu_angelo]

📢 Check out the S2RAI Track (at SAC26 Conference)!

I am co-chairing with Simon Burton, Marta Catillo, Jeremie Guiochet, and Tommaso Zoppi

🗓️ Deadline: September 26th, 2025

ℹ️ Info here: https://t.co/vihwOBoCnq

🇬🇷 SAC26 Website: https://t.co/4ApQoVQrbT

[Angelo Sotgiu @sotgiu_angelo]

📢 Check out the S2RAI Track (at SAC26 Conference)!

I am co-chairing with Simon Burton, Marta Catillo, Jeremie Guiochet, and Tommaso Zoppi

🗓️ Deadline: September 26th, 2025

ℹ️ Info here: https://t.co/vihwOBoCnq

🇬🇷 SAC26 Website: https://t.co/4ApQoVQrbT

[ELSA - European Lighthouse on Secure and Safe AI @elsa_lighthouse]

Exciting news!🔥 @sAIferLab is organizing a challenge on Robust Android Malware Detection!
Hosted by IEEE #SaTML2025, it aims to evaluate #machinelearning methods in the context of malicious attacks against the #Android systems.

https://t.co/XEV2BK2FYV
Deadline: March 31st

[Konrad Rieck 🌈 @mlsec]

We’re excited to announce this year’s competitions for @satml_conf! 🎉 Get ready for four fun challenges tackling prompt injection, data leakage, membership inference, and malware detection.

https://t.co/BTk2yKPbBG

Let’s dive in!
🧵1/5

[Maura Pintor @maurapintor]

📢We are proud to announce that our paper "Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples " has been accepted to #Neurips2022!

w/ @biggiobattista, @zangobot, @ambrademontis, @sotgiu_angelo, Nicholas Carlini, and Fabio Roli

1/n

[Gabriele Ciravegna @CiraGabriele]

It's with enormous pleasure that I tell you that our last paper has been accepted for publication in the IEEE TPAMI, the most important Machine Learning journal. Check it out!
https://t.co/PwTNeN1Q3b
@biggiobattista @StefanoMelacci @sotgiu_angelo @KleineBottleM @ambrademontis

[Battista Biggio @biggiobattista]

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

We investigate how injecting domain-knowledge constraints (first-order logic relationships among classes) into the learning process can help detect adversarial examples.

https://t.co/dDOWOVuyWM

[arXiv Daily @Arxiv_Daily]

Deep Neural Rejection against Adversarial Examples
https://t.co/OPJVHBVKQu
by Angelo Sotgiu et al. including @biggiobattista
#NeuralNetwork #ComputerScience

[Ambra @ambrademontis]

We've developed deep neural rejection (DNR), a mechanism to detect adversarial examples. It rejects samples that exhibit anomalous feature representations at different network layers.

Paper: https://t.co/P0Nv876EKu
Code will be released as part of secml: https://t.co/vDaNmXmpFR