It is our pleasure to invite you to submit a chapter for inclusion in the “Adversarial Multimedia Forensics” book to be Published by Springer – Advances in Information Security. The submitted chapter should have 15-20 pages of single-space single-column in latex and include sufficient details to be useful for Cybersecurity Applications experts and readers with particular reference to multimedia forensics and security. The book is divided into three main sections: (I) Multimedia forensics, (II) Counter-Forensics, and (III) Anti-Counter-Forensics.

We are looking for chapter that apply security and attacks concepts, methods, and techniques to the image and video that are listed in our Call, and NOT applying some of our areas to security (Computer Security). For example, for a multimedia forensics, one example related to Fine-grained CFA artifact assessment for forgery detection or Source camera identification. Another example regarding counter-forensics, applying adversarial attacks on a medical images, or considering the transferability of adversarial attacks against vision transformers. Concerning Anti-Counter Forensics in a exploratory attacks, one example can be related to adversary-aware double JPEG-detector via selected training on attacked samples or resistant to JPEG compression image contrast manipulation identification. Regarding Causative attacks, one example can be proposing defense strategy against poisoning attacks on satellite imagery models. Due to the outstanding capabilities of current Machine learning (ML) algorithms, ML is becoming the de-facto for Multimedia Forensics (MF). However, the inherent fragility of ML architectures creates new, significant security vulnerabilities that prevent their use in security critical applications like MF, where the potential existence of an adversary cannot be ignored. However, given the weakness of the traces that forensic techniques rely on, disabling forensic analysis proves to be a simple task. Therefore, development of novel strategies capable of enhancing the protection of ML-based methods, as well as the assessment of their security in the presence of an adversary, are thus of greatest importance. Thus, it has become essential in MF to develop solutions capable of overcoming the security constraints of ML models used as counter-forensics techniques. This book contributes to the aforementioned goal by emphasizing on image manipulation detection using ML/DL algorithms for MF in adversarial environments. The main structure of the book is divided into the following three sections: (I) presents different methodologies in multimedia forensics; (II) and discusses general concepts and terminology in the field of adversarial machine learning (Adv-ML), with a focus on the concern of counter-forensics (CF), and anti-counter forensics.

Submission: There are no submission or acceptance fees for manuscripts submitted to this book for publication. All manuscripts are accepted based on a double-blind peer-review editorial process. Please send your manuscript *.pdf, *.tex to the e-mail address of one of the editors (ehsan.nowroozi@eng.bau.edu.tr, Alireza.jolfaei@flinders.edu.au, Kassem.kallas@inria.fr)

Timeline: Expression of interest: 15-Jan-2023 (tentative: chapter title, and abstract): Send by email to editors, Selection of chapters: 30-Jan-2023 (Inform to Authors by email and share Easy Chair link with authors for submission), Deadline for full chapter submission: 30-Feb-2023 (Submit via Easy Chair), Review of chapters: 30-Mar-2023, Camera-ready version: 20-April-2023 (Submit in Easy chair)