Leveraging Deep Learning-Assisted Attacks against Image Obfuscation via Federated Learning
Speaker: Jimmy Tekli (BMW Group & Université de Franche-Comté, France)
Title: Leveraging Deep Learning-Assisted Attacks against Image Obfuscation via Federated Learning
Time: Wednesday, 2021.05.19, 10:00 a.m. (CET)
Place: fully virtual (contact Jakub Lengiewicz to register)
Format: 30 min. presentation + 30 min. discussion
Abstract: Federated learning (FL) has recently gained much attention as a machine learning setting where multiple clients collaborate in solving a machine learning problem under the coordination of a central server/coordinator. Each client’s raw data is stored locally without being exchanged nor transferred to the central server; instead, the model’s parameters are shared/aggregated and used to achieve the learning objective. Throughout this seminar, we first present the FL concept, the Federated Averaging algorithm, the FL client/server architecture along with its challenges/limitations and applications. Second, we demonstrate how we employed FL as a collaborative adversarial strategy to leverage deep learning-assisted attacks against obfuscated (e.g. blurred) face images.