多媒體資訊學習與安全

Multimedia Information Learning and Security

學期

113-2

學分

0 學分

當期課號

639011

永久課號

AICA30038

開課單位

智慧科學暨綠能學院

授課教師

許志仲

校區

歸仁

類別

選修

上課時間表

節	週三
5 13:20–14:10	多媒體資訊學習與安全 CM217（歸仁） 3 節連堂
6 14:20–15:10
7 15:30–16:20

* 根據陽明交大上課時間表所列

概述

This course provides a comprehensive introduction to deep learning (DL) and its applications in multimedia security. The first part (Weeks 1-7) covers fundamental deep learning techniques, including image classification, detection, segmentation, restoration, and multi-dimensional image analysis. The second part (Weeks 8-15) explores security challenges in multimedia processing, such as Deepfake detection, adversarial attacks, multimedia forensics, and privacy-preserving AI techniques like federated learning. [Important] This is an implementation-heavy course. Each week includes hands-on assignments, requiring students to build and evaluate deep learning models. Students should anticipate significant computational workloads and plan their resources accordingly.

先修科目

Linear algebra, calculas

備註

無備註

教學方式

Lecture

評分方式

Midterm 10%, Assignment 70%, Final Project 20%

課程大綱

教師未提供此項資料

週次計畫

週次	主題
第 1 週	Course Introduction: Overview of objectives, grading, and an introduction to deep learning and multimedia security.
第 2 週	Deep Learning Fundamentals: Neural networks, backpropagation, and core architecture concepts.
第 3 週	Convolutional Neural Networks (CNNs): Image processing and classification applications.
第 4 週	Optimization & Training Techniques: Loss functions, gradient descent, regularization, transfer learning.
第 5 週	Object Detection & Semantic Segmentation: Overview of Faster R-CNN, YOLO, FCN, DeepLabv3+, SAMv2/v2, UniDet, etc.
第 6 週	Image Restoration & Super-Resolution: Introduction to DIP, SRCNN, EDSR, GAN-based, and DRCT restoration.
第 7 週	Foundation Models: Overview of ViT, Swin, DINO, and their applications in computer vision.
第 8 週	Multi-Dimensional Image Analysis: Medical imaging (MRI/CT) and hyperspectral imaging techniques.
第 9 週	Midterm
第 10 週	Deepfake Detection: Methods to identify and prevent Deepfake media.
第 11 週	Adversarial Attacks & Defenses: Generating adversarial examples and implementing defensive strategies.
第 12 週	Multimedia Forensics: Hyperspectral image forensics and security analysis.
第 13 週	Robust Deep Learning: Techniques to improve model reliability and security against adversarial threats.
第 14 週	Federated Learning & Security: Privacy protection, collaborative learning, and distributed security.
第 15 週	Industry Expert Talk: Exploring cutting-edge developments in multimedia security.
第 16 週	Final Project Submission: Students present their research and security solutions.

教科書

n/a

Office Hours

地點: Office 208
時間: 1100am at 208 office
聯絡方式: chihchung [at] nycu.edu.tw