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AL4MUR 2022 : ICMR 2022 Special Session on Adversarial Learning for Multimedia Understanding and Retrieval
AL4MUR 2022 : ICMR 2022 Special Session on Adversarial Learning for Multimedia Understanding and Retrieval

AL4MUR 2022 : ICMR 2022 Special Session on Adversarial Learning for Multimedia Understanding and Retrieval

Newark, NJ, USA
Event Date: June 27, 2022 - June 30, 2022
Submission Deadline: January 20, 2022
Notification of Acceptance: March 30, 2022




Call for Papers

Call for Papers

Special Session: Adversarial Learning for Multimedia Understanding and Retrieval
Special Session Homepage: https://al4mur.github.io/
Conference: ACM International Conference on Multimedia Retrieval (ICMR 2022)
Location/Date: Newark, NJ, USA; June 27-30, 2022
Conference Homepage: https://www.icmr2022.org/

Introduction:
=============
Nowadays we are living in the era of big multimedia data. The adversarial characteristic naturally lives in many multimedia understanding and retrieval models. Adversarial machine learning has become a cutting-edge technique that attempts to fool the learning systems by generating deceptive data with imperceptible perturbations. Particularly, the existence of adversarial examples destroys the reliability and robustness of deep neural networks, which almost impedes the practical deployment of deep learning models in various multimedia systems. It has been well demonstrated that the existing deep learning models are vulnerable to carefully crafted attacks from malicious adversaries. Moreover, such a security challenge goes well beyond the simple multimedia systems that could all potentially be subject to adversarial attacks.

This ICMR special session is devoted to the publication of high-quality research papers on adversarial learning for various multimedia understanding and retrieval models. The special session will seek original contributions, which address the key challenges and problems. The topics of interest include, but are not limited to:

# Adversarial learning for large-scale image/video/music/audio retrieval
# Adversarial learning for cross-modal analysis and retrieval
# Adversarial learning for image/video classification/detection/tracking
# Adversarial learning techniques on multi-modal learning
# Advanced techniques for generating adversarial examples
# Adversarial attacks and defenses in multi-modal medical analysis
# Adversarial attacks and defenses for autonomous vehicles
# Privacy protection architectures in multi-modal data analysis
# Adversarial examples for multi-modal biomedical tasks
# Optimization algorithms in adversarial attacks and defenses
# Security issues in deep learning-based multimedia database systems
# New benchmark datasets for multimedia adversarial learning

Important Dates:
================
# Paper Submission Deadline: January 20, 2022
# Notification of Acceptance: March 30, 2022
# Deadline for Camera Ready: TBD
# Main Conference: June 27-30, 2022

Submission Guidelines:
======================
Each full paper should be limited to 6-8 pages (6 pages limit + references). Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Session. Following the reviewing procedure of ICMR, all submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers.

Submissions should conform to the submission instructions of the ICMR 2022 Paper submission section (https://www.icmr2022.org/authors/submissions/).

Special Session Organizers:
===========================
# Zheng Zhang, A/Prof., Harbin Institute of Technology, Shenzhen, China, email: [email protected]
# Lei Zhu, Prof., Shandong Normal University, China, email: [email protected]
# Shuihua Wang, Dr., University of Leicester, UK, email: [email protected]
# M. Emre Celebi, Prof., University of Central Arkansas, USA, email: [email protected]


Credits and Sources

[1] AL4MUR 2022 : ICMR 2022 Special Session on Adversarial Learning for Multimedia Understanding and Retrieval


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