Topic 1: Machine Learning Based Techniques for Image and Video Processing
Topic 2: Deepfake Detection and Image 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.
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
List of Publications
Click here to view the publications in Topic Collection "Deepfake Detection and Image Processing".
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