ÖN EĞİTİMLİ EVRİŞİMLİ SİNİR AĞLARI DESTEKLİ GÖRÜNTÜ SAHTECİLİK TESPİTİ YÖNTEMİ

Year 2020, , 705 - 714, 07.08.2020

Ertuğrul Gül , Serkan Öztürk

https://doi.org/10.28948/ngumuh.654519

Abstract

İnternet ve bilgisayar teknolojilerinin gelişmesi ile görüntü sahteciliği tespiti önem kazanmıştır. Ayrıca, görüntü iyileştirme uygulamalarında kullanılan tekniklerin iyi başarım göstermesi için görüntülere uygulanan saldırı çeşitlerinin ve bölgelerinin doğru bir şekilde tespit edilmesi gerekmektedir. Bu çalışmada, görüntülere uygulanan saldırı çeşitlerini ve saldırı bölgelerini tespit etmek için ön eğitimli AlexNet ve GoogLeNet evrişimli sinir ağları destekli görüntü sahtecilik tespiti yöntemi önerilmiştir. Öncelikle; MICC-F2000 veri kümesinde bulunan görüntüler kullanılarak orijinal ve saldırılmış görüntülerin olduğu görüntü sahteciliği tespiti veri kümesi oluşturulmuştur. Saldırılmış görüntüleri elde etmek için Gauss bulanıklaştırma, medyan filtreleme, Gauss gürültü ekleme, Poisson gürültü ekleme ve keskinleştirme saldırıları kullanılmıştır. Daha sonra, ön eğitimli AlexNet ve GoogLeNet ağlarının tam bağlantılı katmanları deneysel veri kümesindeki altı veri sınıfı için yeni tam bağlantılı katmanlar ile değiştirilmiştir. Oluşturulan AlexNet ve GoogLeNet destekli ağlar hazırlanan görüntü sahteciliği tespiti veri kümesi ile eğitilerek test edilmiştir. Faklı hiperparametre değerleri için ağların başarımları ölçülmüştür. AlexNet destekli ağlarda en yüksek başarım %99,48’lik doğruluk oranı ile elde edilirken, GoogLeNet destekli ağlarda ise en yüksek başarım %99,92’lik doğruluk oranı ile elde edilmiştir. Ayrıca, geliştirilen AlexNet ve GoogLeNet destekli sahtecilik tespiti yönteminin CoMoFoD veri kümesinden alınan görüntüler üzerindeki saldırıları tespit edebilme başarısı gözlemlenmiştir. Deneysel sonuçlar önerilen yöntemin başarılı bir şekilde görüntü sahteciliği tespiti için kullanılabileceğini göstermiştir.

Keywords

evrişimli sinir ağı, sahtecilik tespiti, transfer öğrenme, AlexNet, GoogLeNet

PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK BASED IMAGE TAMPER DETECTION METHOD

Abstract

With the development of internet and the computer technologies, image forgery detection has become important issue. In addition, in order to obtain successful performance in image enhancement techniques, the types and regions of the attacks applied to the images must be determined correctly. In this study, in order to detect the types and regions of the attacks applied to the images, pre-trained AlexNet and GoogLeNet convolutional neural networks-based forgery detection method has been proposed. Firstly, image forgery detection dataset containing the original and the attacked images has been created using the images in the MICC-F2000 dataset. Gaussian blurring, median filtering, Gaussian noise adding, Poisson noise adding and sharpening attacks have been used to obtain the attacked images. Then, the fully connected layers of the pre-trained AlexNet and GoogLeNet networks have been replaced with the new fully connected layers for the six classes of the created image forgery detection dataset. The modified AlexNet and GoogLeNet based networks have been trained and tested with the created image forgery detection dataset. The networks performances have been evaluated for different hyper parameter values. While the highest accuracy rate of 99.48% has been achieved in AlexNet supported networks, the highest accuracy rate of 99.92% has been achieved in GoogLeNet supported networks. Also, the proposed AlexNet and GoogLeNet-based forgery detection method has been tested on the images from CoMoFoD dataset. Experimental results show that the proposed method can be used successfully for the image forgery detection.

Keywords

Convolutional neural network, Tamper detection, Transfer learning, AlexNet, GoogLeNet

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