A
Asifullah Khan
Researcher at Pakistan Institute of Engineering and Applied Sciences
Publications - 232
Citations - 7325
Asifullah Khan is an academic researcher from Pakistan Institute of Engineering and Applied Sciences. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 38, co-authored 192 publications receiving 5109 citations. Previous affiliations of Asifullah Khan include Gwangju Institute of Science and Technology & Ghulam Ishaq Khan Institute of Engineering Sciences and Technology.
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Journal Article
A Secure Semi-Fragile Watermarking Scheme for Authentication and Recovery of Images based on Wavelet Transform
TL;DR: A secure semi-fragile watermarking, with a choice of two watermarks to be embedded, in integer wavelet domain and makes use of semi fragile watermarks for achieving better robustness.
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Low complexity versatile video coding for traffic surveillance system
TL;DR: A content oriented adaptive search range setting algorithm, where the search range size of the children coding units (CUs) can be adaptively set by using the best motion vector information of their parent CU, to improve the encoding complexity of VVC.
Journal ArticleDOI
Prediction of GPCRs with pseudo amino acid composition: employing composite features and grey incidence degree based classification.
Zia-ur-Rehman,Asifullah Khan +1 more
TL;DR: The classification in the present research is performed using grey incidence degree (GID) measure, which can efficiently analyze the numerical relation between various components of GPCRs.
Proceedings ArticleDOI
Hiding depth map of an object in its 2D image: Reversible watermarking for 3D cameras
TL;DR: This work proposes a method to hide depth map, as a watermark, in its corresponding 2D image, to serve two purposes; protection of the captured image and secure transmission of its depth map.
Journal ArticleDOI
Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
Muhammad Asam,Shaik Javeed Hussain,Mohammed Mohatram,Saddam Hussain Khan,Tauseef Jamal,Amad Zafar,Asifullah Khan,Muhammad Umair Ali,Umme Zahoora +8 more
TL;DR: In this article, the authors proposed two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space Based Malware Classification (DBFS-MC).