Theses

Συλλογή και ανάλυση δεδομένων επίδοσης από Raspberry Pi και Jetson Nano κατά τη διάρκεια εκτέλεσης αλγορίθμων νευρωνικών δικτύων.

Στην εργασία αυτή θα πρέπει να εκτελέσετε (inference) διάφορους αλγόριθμους νευρωνικών δικτύων βαθιάς μάθησης για συγκεκριμένα δεδομένα και κάτω από συγκεκριμένες συνθήκες προκειμένου να καταγράψετε και εν συνεχεία να αναλύσετε τις τιμές επίδοσης διαφόρων συστημάτων (ειδικά Raspberry PI και Jetson Nano). Αναζήτηση συσχετισμών μεταξύ χαρακτηριστικών μοντέλων νευρωνικών δικτύων βαθιάς μάθησης και επίδοσης συστήματος κατά την εκτέλεση του μοντέλου.

Generate Adversarial Examples with GANs

Generative Models such as Generative Adversarial Networks (GANs) can be leveraged to generate more realistic adversarial examples.This task focuses on exploring the potential of GANs to create adversarial examples capable of deceiving machine learning models into misclassifying the provided samples.

Reference: Xiao, C. et al. (2018) ‘Generating adversarial examples with adversarial networks’ , Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 3905–3911. doi:10.24963/ijcai.2018/543.

Use Generative Adversarial Networks (GANs) to protect classifiers against adversarial attacks.

This task focuses on employing GANs as a defense mechanism against adversarially perturbed samples. A GAN can be trained to model the distribution of clean, unperturbed data. Given a perturbed image, the model finds a close output which does not contain the adversarial changes and, as a result, purifies the attacked sample.

Reference: Dai, X., Liang, K. and Xiao, B. (2024) ‘Advdiff: Generating unrestricted adversarial examples using diffusion models’, Lecture Notes in Computer Science, pp. 93–109. doi:10.1007/978-3-031-72952-2_6.

Generate Adversarial Examples with Diffusion Models

The objective of this task is to utilize Diffusion Models to generate adversarial examples aimed at causing misclassification. Diffusion models can achieve adversarial attacks by incorporating adversarial objectives in their generative process.

Reference: Samangouei, P. (2018) ‘Defense-GAN: Protecting classifiers against adversarial attacks using generative models’ , arXiv preprint, arXiv:1805.06605.

The use of Diffusion Models for Adversarial Purification

In this task, the main purpose is to use Diffusion Models in order to purify adversarial samples. The given adversarial sample is diffused with noise following a forward diffusion process, and then a clean image is recovered following a reverse generative process.

Reference: Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A. and Anandkumar, A. (2022) ‘Diffusion models for adversarial purification’ , arXiv preprint, arXiv:2205.07460.

Comparative study of the convergence speed Vs resilience tradeoff in model aggregation mechanisms in Federated Learning