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Central Symmetric Local Binary Pattern Based Image Forgery Detection

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 288 - 294, 20.10.2021
https://doi.org/10.53070/bbd.990064

Öz

With the progress in multimedia editing tools, fraud operations, especially on images, have begun to be encountered frequently. For this purpose, in recent years, many new algorithms are needed in computer science under image forgery detection. In this study, the Central Symmetric Local Binary Pattern algorithm, which is a successful texture extraction algorithm, to the best of our knowledge, that has not been used in this field before, has been tested on a current image dataset.

Kaynakça

  • [1] Gurunlu, B., Ozturk, S., “A Survey on Photo Forgery Detection Methods”, ITM Web of Conferences CMES 2018, vol.22, pp.1-5, 2018.
  • [2] Fridrich J, Soukal D, LukášJ . Detection of copy-move forgery in digital images. Digit Forensic Res Work 2003; 3:652–63, 2003
  • [3] Meena KB, Tyagi V. Image forgery detection : survey and future directions, Data, Engineering and Applications, 2. Singapore: Springer; . p. 163–95, 2009
  • [4] Cozzolino D , Poggi G , Verdoliva L . Efficient dense-field copy-move forgery detection. IEEE Transaction on Information Forensics Security;10(11):2284–97, 2015
  • [5] Wang X-Y , Jiao L-X , Wang X-B , Yang H-Y , Niu P-P . Copy-move forgery detection based on compact color content descriptor and delaunay triangle matching. Multimed Tools Appl ;78(2):2311–44, 2018
  • [6] Al-Qershi OM , Khoo BE . Enhanced block-based copy-move forgery detection using k-means clustering. Multidimensional System Signal Process ;30:1671–95, 2018
  • [7] Chen B , Yu M , Su Q , Li L . Fractional quaternion cosine transform and its application in color image copy-move forgery detection. Multimedia Tools Application ;78:8057–73, 2018
  • [8] Mahmood T , Irtaza A , Mehmood Z , Tariq Mahmood M . Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int Oct.;279:8–21, 2017
  • [9] Meena KB , Tyagi V . A copy-move image forgery detection technique based on Gaussian-Hermite moments. Multimed Tools Application ;78:33505–26, 2019
  • [10] Pan X , Lyu S . Region duplication detection using image feature matching. IEEE Transaction on Information Forensics Security ;5:857–67, 2010 .
  • [11] Pun C , Yuan X , Bi X . Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Transaction on Information Forensics Security;10(8):1705–16, 2015.
  • [12] Liu K , et al. Copy move forgery detection based on keypoint and patch match. Multimed Tools Application:82–4, 2019.
  • [13] Heikkilä M., Pietikäinen M., Schmid C. Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: Kalra P.K., Peleg S. (eds) Computer Vision, Graphics and Image Processing, 2006
  • [14] Ojala, T., Pietikainen, M., Harwood, D., "A Comparative Study of Texture Measures with Classification Based on Feature Distributions,", Pattern Recognition ,29:51-59, 1996.
  • [15] Tralic, D., Zupancic, I., Grgic S., Grgic M., CoMoFoD - New Database for Copy-Move Forgery Detection, in Proc. 55th International Symposium ELMAR-2013, pp. 49-54, September 2013.

Merkezi Simetrik Yerel İkili Örüntü Temelli Görüntü Sahteciliği Tespiti

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 288 - 294, 20.10.2021
https://doi.org/10.53070/bbd.990064

Öz

Multimedya düzenleme araçlarındaki ilerleme ile birlikte günümüzde özellikle görüntüler üzerinde sahtecilik işlemleri sıklıkla karşılaşılmaya başlanmıştır. Buna yönelik olarak son yıllarda, bilgisayar bilimlerinde, görüntü sahteciliği altında birçok yeni algoritmaya ihtiyaç duyulmaktadır. Bu çalışmada daha önce bu alanda kullanılmamış, başarılı bir doku çıkarım algoritması olan, Merkezi Simetrik Yerel İkili Örüntü algoritması güncel bir görüntü veriseti üzerinde denenmiştir.

Kaynakça

  • [1] Gurunlu, B., Ozturk, S., “A Survey on Photo Forgery Detection Methods”, ITM Web of Conferences CMES 2018, vol.22, pp.1-5, 2018.
  • [2] Fridrich J, Soukal D, LukášJ . Detection of copy-move forgery in digital images. Digit Forensic Res Work 2003; 3:652–63, 2003
  • [3] Meena KB, Tyagi V. Image forgery detection : survey and future directions, Data, Engineering and Applications, 2. Singapore: Springer; . p. 163–95, 2009
  • [4] Cozzolino D , Poggi G , Verdoliva L . Efficient dense-field copy-move forgery detection. IEEE Transaction on Information Forensics Security;10(11):2284–97, 2015
  • [5] Wang X-Y , Jiao L-X , Wang X-B , Yang H-Y , Niu P-P . Copy-move forgery detection based on compact color content descriptor and delaunay triangle matching. Multimed Tools Appl ;78(2):2311–44, 2018
  • [6] Al-Qershi OM , Khoo BE . Enhanced block-based copy-move forgery detection using k-means clustering. Multidimensional System Signal Process ;30:1671–95, 2018
  • [7] Chen B , Yu M , Su Q , Li L . Fractional quaternion cosine transform and its application in color image copy-move forgery detection. Multimedia Tools Application ;78:8057–73, 2018
  • [8] Mahmood T , Irtaza A , Mehmood Z , Tariq Mahmood M . Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int Oct.;279:8–21, 2017
  • [9] Meena KB , Tyagi V . A copy-move image forgery detection technique based on Gaussian-Hermite moments. Multimed Tools Application ;78:33505–26, 2019
  • [10] Pan X , Lyu S . Region duplication detection using image feature matching. IEEE Transaction on Information Forensics Security ;5:857–67, 2010 .
  • [11] Pun C , Yuan X , Bi X . Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Transaction on Information Forensics Security;10(8):1705–16, 2015.
  • [12] Liu K , et al. Copy move forgery detection based on keypoint and patch match. Multimed Tools Application:82–4, 2019.
  • [13] Heikkilä M., Pietikäinen M., Schmid C. Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: Kalra P.K., Peleg S. (eds) Computer Vision, Graphics and Image Processing, 2006
  • [14] Ojala, T., Pietikainen, M., Harwood, D., "A Comparative Study of Texture Measures with Classification Based on Feature Distributions,", Pattern Recognition ,29:51-59, 1996.
  • [15] Tralic, D., Zupancic, I., Grgic S., Grgic M., CoMoFoD - New Database for Copy-Move Forgery Detection, in Proc. 55th International Symposium ELMAR-2013, pp. 49-54, September 2013.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm PAPERS
Yazarlar

Bilgehan Gürünlü 0000-0001-9185-1908

Serkan Öztürk 0000-0002-0309-3420

Yayımlanma Tarihi 20 Ekim 2021
Gönderilme Tarihi 2 Eylül 2021
Kabul Tarihi 16 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special

Kaynak Göster

APA Gürünlü, B., & Öztürk, S. (2021). Merkezi Simetrik Yerel İkili Örüntü Temelli Görüntü Sahteciliği Tespiti. Computer Science, IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium(Special), 288-294. https://doi.org/10.53070/bbd.990064

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