Pregled bibliografske jedinice broj: 881114
Evaluating Robustness of Perceptual Image Hashing Algorithms
Evaluating Robustness of Perceptual Image Hashing Algorithms // Proceedings of the International Conference on Computers in Technical Systems MIPRO 2017
Opatija, 2017. str. 1186-1191 doi:10.23919/MIPRO.2017.7973569 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 881114 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Evaluating Robustness of Perceptual Image Hashing Algorithms
Autori
Drmić, Andrea ; Šilić, Marin ; Delač, Goran ; Vladimir, Klemo ; Kurdija, Adrian Satja
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the International Conference on Computers in Technical Systems MIPRO 2017
/ - Opatija, 2017, 1186-1191
Skup
40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Mjesto i datum
Opatija, Hrvatska, 22.05.2017. - 26.05.2017
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Perceptual Hashing, Image Identification, Robustness
Sažetak
In this paper we evaluate the robustness of perceptual image hashing algorithms. The image hashing algorithms are often used for various objectives, such as images search and retrieval, finding similar images, finding duplicates and near-duplicates in a large collection of images, etc. In our research, we examine the image hashing algorithms for images identification on the Internet. Hence, our goal is to evaluate the most popular perceptual image hashing algorithms with the respect to ability to track and identify images on the Internet and popular social network sites. Our basic criteria for evaluation of hashing algorithms is robustness. We find a hashing algorithm robust if it can identify the original image upon visible modifications are performed, such as resizing, color and contrast change, text insertion, swirl etc. Also, we want a robust hashing algorithm to identify and track images once they get uploaded on popular social network sites such as Instagram, Facebook or Google+. To evaluate robustness of perceptual hashing algorithms, we prepared an image database and we performed various physical image modifications. To compare robustness of hashing algorithms, we computed Precision, Recall and F1 score for each competing algorithm. The obtained evaluation results strongly confirm that P-hash is the most robust perceptual hashing algorithm.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2014-09-9606 - Sustav predlaganja u arhitekturi zasnovanoj na uslugama (RSOA) (Srbljić, Siniša, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Klemo Vladimir
(autor)
Goran Delač
(autor)
Marin Šilić
(autor)
Adrian Satja Kurdija
(autor)