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Author Guidelines

ubmitted papers are assumed to contain no proprietary material unprotected by patent or patent application; responsibility for technical content and for protection of proprietary material rests solely with the author(s) and their organizations and is not the responsibility of JOIG or its Editorial Staff. The main author is responsible for ensuring that the article has been seen and approved by all the other authors. It is the responsibility of the author to obtain all necessary copyright release permissions for the use of any copyrighted materials in the manuscript before the submission.

Authors are requested to follow JOIG guidelines for preparing their manuscripts. An article sample template can be found here (MS Word).



Submission Preparation Checklist

All submissions must meet the following requirements.

  • The submission has been prepared in accordance with the journal's Instructions for Authors and follows all the journals' policy requirements.
  • All authors are listed, each author has participated sufficiently in the work to take public responsibility for the content or part of it, and each author has approved the final version of the manuscript.
  • No part of the manuscript has been, or will be, published elsewhere nor is under consideration for publication elsewhere.

Topic: Machine Learning Based Techniques for Image and Video Processing

Machine learning-based techniques have been widely used in various computer vision, automation, image and video processing applications, leading to leapfrogging improvements in performance. We would like to gather researchers here to demonstrate the latest research and approaches regarding various aspects of image and video processing in the era of Big Data. 
This topic invites researchers to contribute original research articles that stimulate the continuing efforts to understand image and video processing algorithms, data structures, optimization trade-offs, architectures, and their applications.

Topic: Deepfake Detection and Image Processing

Deepfake Detection is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image. The goal of Deepfake Detection and Image Processing is to identify such manipulations and distinguish them from real videos or images.

We welcome manuscripts on all aspects of the Deepfake creation and detection domain, including image processing, adversarial forensics, and generative models on images and video. Topics of interest include, but are not limited to:

Source image reconstruction from Deepfakes

  • Generative model recognition
  • Adversarial forensics on deepfake content
  • Generative models for deepfake creation
  • Image/video forgery creation and detection
  • Facial manipulation and synthesis techniques
  • Image/video Deepfake detection
  • Identification and localization of the manipulated Region of Interest (ROI)
  • Detection of structural/textural changes in an image due to forgery or manipulation
  • Detection of post processing effects from Deepfake generation
  • Multiscale and multimodal transformers for Deepfake detection
  • Visual cryptography and watermarking techniques for authentication and forgery detection
  • Attention and capsule networks for Deepfake detection
  • Morphing and Deepfake Attacks on facial recognition systems

Selected Papers from Conferences

This section will publish selected papers from conference.

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