[visionlist] Handbook of DeepFakes and Face Manipulations - Call for Chapters

Zahid Akhtar zahid.eltc at gmail.com
Fri Feb 26 21:36:11 -04 2021

Please can you post this on visionlist. Thank you.

*Title: Handbook of DeepFakes and Face Manipulations *- Call for Chapters


*Editors: **Zahid Akhtar1, Roy Shilkrot2, and Pattie Maes3 *

*1 **State University of New York Polytechnic Institute, USA*

*2 **Stony Brook University, USA*

*3 **Massachusetts Institute of Technology (MIT), USA*


Machine learning-based techniques are being utilized to generate
hyper-realistic manipulated facial multimedia contents, known as DeepFakes.
While such technologies have positive potentials for entertainment
applications, malevolent use can harm citizens and the society at large by
constructing indecent content, spreading fake news to subvert elections or
undermine politics, bullying people and ameliorating social engineering to
perpetrate financial frauds. In fact, it has been shown that manipulated
facial multimedia contents not only can deceive the humans but also
automated face recognition based biometric systems. Advent of advanced
hardware, powerful smart devices, user-friendly apps (e.g., FaceApp, ZAO)
and open-source ML codes (e.g., Generative Adversarial Networks) have
enabled even non-experts to effortlessly create manipulated facial
multimedia contents. In principle, face manipulation involves swapping two
faces, modifying facial attributes (e.g., age and gender), morphing two
different faces into one face, adding imperceptible perturbations (i.e.,
adversarial examples), synthetically generating faces, or
animating/reenacting facial expressions in the face images/videos.

In recent years, a number of articles have been published on the topic
of DeepFakes
and face manipulation generation, detection, and face recognition under
manipulations. This book will provide the first comprehensive account of
the state-of-the-art in DeepFakes and face manipulations. The book will
include an introductory chapter by the editors that will summarize the
state-of-the-art in DeepFakes and face manipulations, followed by
individual chapters describing the various works that are being used for
such purposes, the methods that have been devised to generate DeepFakes,
facial sample editing using audio and video modalities, analysis of face
recognition systems’ robustness, and usability of methods.

This handbook on “DeepFakes and Face Manipulations” is expected to be
published in the Springer Advances in Computer Vision and Pattern
Recognition series in 2021. The editorial team is soliciting chapter
contributions from the machine learning (ML), computer vision, biometrics,
multimedia forensics, pattern recognition, and artificial intelligence (AI)
research communities.

 Topics of interest include, but are not limited to:

•       Generation of DeepFakes, and face morphing, manipulation and
adversarial attacks

•       Generation of synthetic faces using ML/AI techniques, e.g., GANs

•       Detection of DeepFakes, and face morphing, manipulation and
adversarial attacks, including generalizable systems

•       Generation and detection of audio DeepFakes

•       Novel datasets and experimental protocols to facilitate research in
DeepFakes and face manipulations

•       Formulation and extraction of DeepFakes device, platform,
software/app fingerprints

•       Face recognition systems (and humans) against DeepFakes, and face
morphing, manipulation and adversarial attacks, including their
vulnerabilities to digital face manipulations

•       DeepFakes in the courtroom and on copyright law


*Originality *


Chapter contributions should contain 25-30% novel content compared to
earlier published work by the authors.




•       Expression of interest: 04-15-2021 (tentative: chapter title, and

•       Selection of chapters: 05-30-2021

•       Deadline for full chapter submission: 07-30-2021

•       Review of chapters: 08-30-2021

•       Camera-ready version: 09-31-2021

Prospective authors should express their interest by May 15th 2021 with a
concise chapter proposal (title, authors, and abstract) to the editorial

Zahid Akhtar (akhtarz at sunypoly.edu)

Roy Shilkrot (roy.shilkrot at stonybrook.edu)

*Pattie Maes *(pattie at mit.edu)
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