Academic Editor
Author
Contributions by role
Author 1
Editor 1
Arshad Ahmad
Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Pakistan
Summary
Edited Journals
ICCK Contributions

Open Access | Review Article | 30 June 2025
A Comprehensive Survey of DeepFake Generation and Detection Techniques in Audio-Visual Media
ICCK Journal of Image Analysis and Processing | Volume 1, Issue 2: 73-95, 2025 | DOI: 10.62762/JIAP.2025.431672
Abstract
The rapid advancement in machine learning and artificial intelligence has significantly enhanced capabilities in multimedia content creation, particularly in the domain of deepfake generation. Deepfakes leverage complex neural networks to create hyper-realistic manipulated audio-visual content, raising profound ethical, societal, and security concerns. This paper presents a comprehensive survey of contemporary trends in deepfake video research, focusing on both generation and detection methodologies. The study categorizes deepfakes into three primary types: facial manipulation, lip-synchronization, and audio deepfakes, further subdividing them into face swapping, face generation, attribute m... More >

Graphical Abstract
A Comprehensive Survey of DeepFake Generation and Detection Techniques in Audio-Visual Media

Open Access | Research Article | 20 March 2025
Plant Disease Detection Using Deep Learning Techniques
ICCK Journal of Image Analysis and Processing | Volume 1, Issue 1: 36-44, 2025 | DOI: 10.62762/JIAP.2025.227089
Abstract
Plant diseases create one of the most serious risks to the world's food supply, reducing agricultural production and endangering millions of people's lives. These illnesses can destroy crops, disrupt food supply networks, and increase the danger of food deficiency, emphasizing the importance of establishing strong methods to protect the world's food sources. The approaches of deep learning have transformed the field of plant disease diagnosis, providing sophisticated and perfect solutions for early detection and management. However, a prevalent concern with deep learning models is their susceptibility to a lack of generalization and robustness when faced with novel crop and disease categorie... More >

Graphical Abstract
Plant Disease Detection Using Deep Learning Techniques