Special Session at APSIPA ASC 2024の検討

[M. Kawamura]

山口大学　川村です。

APSIPA ASC 2024 http://www.apsipa2024.org/ が12月3日～6日に
マカオで開催されます。
例年、EMMに参加される方が、APSIPAでも発表される場合が多いので、
Special Sessionを企画したいと思います。

もし発表を予定されている方がおりましたら、一緒に特別セッションを企画しませんか？
特別セッションの企画の提案締切が6月17日となっていますので、そろそろ
準備をしなければなりません。興味がある方はご連絡ください。

また、既に特別セッションを企画されている方は、情報交換できたらと思います。

よろしくお願いします。

[iech...@nii.ac.jp]

川村先生，EMMのみなさま，

下記の件に関連しますが，栗林先生とご一緒に以下の特集号を企画しておりますの
で，
奮ってご投稿頂きますと幸いです．C. C. Jay Kuo先生のご尽力により，APSIPA
Trans．の
Visibilityが飛躍的に上昇しておりますので，何卒宜しくお願い致します．」

越前

[Call for papers]
Themed Series of APSIPA Trans. on Signal and Information Processing on
“Deepfakes, Unrestricted Adversaries, and Synthetic Realities in Generative
AI Era”

Introduction
The rise of generative AI has led to a notable increase in the use of
deepfake technology, accompanied by a concerning surge in unrestricted
adversarial examples. This trend has posed significant concerns in the
signal and information processing community. Initially limited to visual
media, deepfakes have evolved to include multimodal elements, seamlessly
integrating audio, text, and imagery to create elaborate narratives. This
advancement has given rise to synthetic realities where distinguishing
between authentic and manipulated content poses an increasingly formidable
challenge.
Moreover, the proliferation of deepfakes has coincided with unrestricted
adversaries, which exploit inherent vulnerabilities in machine learning
models and intensify existing challenges. While much research has
concentrated on defending against norm-constrained unimodal attacks, it is
crucial to address unrestricted adversaries, which pose significant risks to
the reliability and safety of AI systems across various domains. Expanding
research efforts to encompass uni- and multi-modal adversaries is vital for
developing robust defenses and ensuring the integrity of AI-driven
technologies in real-world applications.
This themed series addresses the two sides of the coin - generation and
defense against deepfakes, unrestricted adversaries, and synthetic
realities. We welcome researchers and innovators to contribute their
original work and help shape the future of information forensics in the
generative AI era. Submissions are invited to explore a broad spectrum of
topics, including but not limited to the following.

Topics of Interest:
・ Uni- or multi-modal deepfake generation and detection
・ Deepfake attribution and source identification techniques
・ Explainable AI methods for understanding and interpreting deepfake
models
・ Ethical and societal implications of deepfake technologies
・ Applications of deepfakes in creative industries (e.g., film,
advertising, entertainment)
・ Generation and detection of uni- or multi-modal unrestricted
adversaries
・ Theoretical analysis of unrestricted adversaries
・ Transferability of unrestricted adversaries across different models
and domains
・ Realistic threat models and defenses
・ Benchmarking and evaluation methodologies and datasets
・ Human perception and cognitive aspects

Each paper submitted to this series will be reviewed using the
first-come-first-serve principle. The target of the first round of
decision-making is 5 weeks, and the period of the first round of revision is
2 weeks. The paper will be accepted between 8-12 weeks (depending on 1 or 2
revisions). Each paper will be published as an open-access article
immediately after its acceptance. Once all papers in this series are
published, they will be assembled into an online book with an editorial
written by the guest editorial team. If a paper cannot be accepted within
the publication window, it will be changed to a regular paper. If you are
interested in paper submission, please refer to:
https://nowpublishers.com/Journal/AuthorInstructions/SIP
If you have any questions, please contact Dr. Isao Echizen (iech...@nii.ac.
jp) and Dr. Minoru Kuribayashi (kmi...@tohoku.ac.jp).

Submission Window: August 1 to October 31, 2024
Publication Window: January 2025

Guest Editorial Team
● Isao Echizen (Co-lead), National Institute of Informatics, Japan
● Minoru Kuribayashi (Co-lead), Tohoku University, Japan
● Yuhong Liu, Santa Clara University, USA
● Huy H. Nguyen, National Institute of Informatics, Japan
CFP link: https://www.nowpublishers.com/Public-Content/CFP-ATSIP-DF-2024.pdf