WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
Browse
4 results
Search Results
Conference Object Citation - WoS: 3Citation - Scopus: 7A Taxonomic Survey of Model Extraction Attacks(IEEE, 2023) Genç, Didem; Özuysal, Mustafa; Tomur, EmrahA model extraction attack aims to clone a machine learning target model deployed in the cloud solely by querying the target in a black-box manner. Once a clone is obtained it is possible to launch further attacks with the aid of the local model. In this survey, we analyze existing approaches and present a taxonomic overview of this field based on several important aspects that affect attack efficiency and performance. We present both early works and recently explored directions. We conclude with an analysis of future directions based on recent developments in machine learning methodology.Article Object Detection With Brief Descriptors and Locality Sensitive Matching for Augmented Reality(Pamukkale Üniversitesi, 2017) Özuysal, MustafaIn this paper, an object detection approach suitable for mobile augmented reality is presented. The baseline approach is bused on matching keypoint descriptors and yerin.,ing these matches with geometric constraints. The performance optimizations necessary for speeding up matching are detailed. It is [ifs demonstrated that it is possible to increase the performance of the Locality Sensitive Hashing by exploiting approaches from the information retrieval field.Conference Object Citation - WoS: 1Citation - Scopus: 2Scene text localization using keypoints(Institute of Electrical and Electronics Engineers Inc., 2015) Erdoğmuş, Nesli; Özuysal, MustafaScene text localization and recognition (also known as text localization and recognition in real-world images, nature scene OCR or text-in-the-wild problem) is an open problem, attracting increasing interest from researchers. In this paper, we address the localization issue and leave the recognition part out of its scope. For the purpose of scene text localization, Scale-Invariant Feature Transform (SIFT) keypoints are extracted from the images and classified as text and non-text. Subsequently, the text keypoints are utilized to compute the bounding boxes around text regions. The proposed technique is tested on the database of ICDAR 2013 Robust Reading Competition-Challenge 2 and the experimental results are reported in detail. Although the idea introduced here is still at its infancy, it is observed to achieve remarkable results and due to the fact that there is a large room for improvement, it is found to be promising.Conference Object A Detailed Analysis of Mser and Fast Repeatibility(Institute of Electrical and Electronics Engineers Inc., 2015) Uzyıldırım, Furkan Eren; Köksal, Ali; Özuysal, MustafaThis paper investigates the relationship between the MSER and FAST repeatability and changes in various camera parameters. By employing a realistic view synthesis methodology, it is possible to observe the effect of small parameter changes on the repeatability. Furthermore, for the analysis of MSER repeatability, a convex hull approach is proposed instead of fitting ellipses to the MSER region. This yields a better approximation to the MSER region without significantly increasing computation time.