Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma

Year 2021, Volume: 9 Issue: 5, 1695 - 1706, 31.10.2021

İsrafil Dilber , Aydın Çetin

https://doi.org/10.29130/dubited.897437

Cited By: 1

Abstract

Son yıllarda teknolojide meydana gelen gelişmelerle beraber başta internet ve sosyal medya olmak üzere bulut bilişim, akıllı telefon ve navigasyon sistemleri gibi uygulamaların kullanım oranları artmıştır. İnternet ve bilişim cihazlarının yoğun kullanımı, beraberinde depolanan veya aktarılan veri miktarını arttırmış ve bu artış aynı zamanda dijital dünya ile ilişkilendirilen suç oranının da yükselmesine neden olmuştur. İşlenen suçlara ilişkin elde edilen delil boyutu da paralel olarak artmış ve artan veri miktarı, adli bilişim uzmanlarının mevcut imkânlarla veriyi analiz edebilmesini zorlaştırmıştır. Adli bilişim veri inceleme süreçlerinde yaşanan aksamalar nihai olarak adli yargılama süreçlerini de olumsuz etkilemiştir. Söz konusu sorunların giderilmesi kapsamında makalede, elde edilen görüntü verilerinin hızlı ve doğru olarak analiz edilmesini sağlayan bir model önerilmiştir. Önerilen model, VGG16 ağ yapısı ile görüntü sınıflandırma için özel tasarlanan ağ katmanlarından oluşmaktadır. Çalışmada, 2085’i Kaggle platformundan 915’i farklı kaynaklardan oluşturulan 300*300 piksel çözünürlüklü resimlerden oluşan veri seti kullanılmıştır. Model, FloydHub ortamında Keras ve TensorFlow kütüphaneleri ile test edilmiştir. Test sonuçlarına göre modelde %97.8 doğruluk oranı elde edilmiştir. Elde edilen sonuç, benzer çalışmalarla kıyaslanmış ve önerilen modelin diğer çalışmalara oranla ortalama %5 oranında performans artışı sağladığı görülmüştür.

Keywords

Adli bilşim, Yapayzeka, Derin öğrenme, Evrişimsel sinir ağları, ESA

Artificial Intelligence in Digital Forensics Investigation Processes: Image Classification with VGG16

Abstract

With the recent developments in technology, the usage rates of cloud computing, smartphones, and navigation systems, especially the internet and social media, have increased. The intensive use of these devices has increased the amount of data stored or transferred. Such an increase has also led to a growth in the digital world-related crime rate. The size of the evidence has grown incrementally, and making it difficult to analyze the data effectively. The failures in analysis processes have ultimately affected the judicial proceedings negatively. To solve the aforementioned problems, a model is proposed that enables fast and accurate analysis of image data in the article. The model consists of VGG16 convolutional and fully connected neural network layers. In the study, a data set consisting of 300x300 pixel resolution images, 2085 of which were compiled from the Kaggle platform and 915 from different sources, was used. The model was tested in the FloydHub with the Keras and TensorFlow libraries. According to the test results, a 97.8% accuracy rate was obtained. Test results indicate that the proposed model provides an average performance increase of 5% compared to other studies.

Keywords

Digital forensics, Artificial intelligence, Deep learning, Convolutional neural networks, CNN

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