Nesne Tespitinde En Uygun Modelin Seçimi İçin Görüntüler Üzerinde Evrişimli Sinir Ağları ile Çekişmeli Saldırı Tespiti

Öz

Görüntülerdeki nesnelerin yüksek doğrulukta tespit edilmesi gerçek zamanlı uygulamalar başta olmak üzere birçok uygulama alanı için önemli bir konudur. Evrişimli sinir ağları ise son yıllarda nesne tespiti uygulamalarında kullanılan ve yüksek doğrulukta başarılar elde edilebilen derin öğrenme tabanlı yöntemlerdir. Klasik Evrişimli sinir ağları orijinal görüntülerdeki nesneleri yüksek doğruluk tespit edebilmesine rağmen ağların FGSM, PGD ve APGD gibi çekişmeli saldırıların uygulandığı görüntülerde başarımları yetersiz kalabilmektedir. Bu problemin üstesinden gelmek için saldırılı görüntülerde nesne tespiti için farklı modeller ve ön işlemler geliştirilmektedir. Ancak saldırılı ve saldırısız durumlar için modellerin başarımları değişebilmektedir. Bu yüzden saldırının olup olmadığının tespit edilmesi ve duruma göre en başarılı modelin seçilmesi gerekmektedir. Bahsedilen problemi çözmek için bu çalışmada görüntülerde çekişmeli saldırı olup olmadığının evrişimli sinir ağları kullanarak tespit edilmesi gerçekleştirilmektedir. Çalışma kapsamında YOLO v5 ve Faster R-CNN modelleri transfer öğrenmeli ve transfer öğrenmesiz olarak çekişmeli saldırı tespiti görevi için eğitilmiştir. Deneysel sonuçlar transfer öğrenmeli Faster R-CNN modelinin 0.971 f1 skoru ile dört model arasından en başarılı sonucu elde ettiğini göstermektedir.

Anahtar Kelimeler

• Nesne tespiti
• Çekişmeli saldırı
• Faster R-CNN
• YOLO v5
• ESA

Adversarial Attack Detection with Convolutional Neural Networks on Images for Selection of the Most Suitable Model in Object Detection

Öz

Object detection on images with high accuracy is an essential issue for many application areas, especially real-time applications. Convolutional neural networks, on the other hand, are deep learning-based methods that have been used in object detection applications in recent years and have achieved high accuracy. However, although classical convolutional neural networks can detect objects on original images with high accuracy, their performance may be insufficient on images where adversarial attacks such as FGSM, PGD, and APGD are applied. To overcome this problem, different models and pre-processes are developed for object detection on attacked images. However, the performance of the models may vary for attacked and non-attacked cases. Therefore, it is necessary to determine whether the attack occurred and select the most successful model according to the case. To solve the problem mentioned above, detect whether there is an adversarial attack on the images using convolutional neural networks has been performed in this study. Within the scope of the study, YOLO v5 and Faster R-CNN models were trained for the adversarial attack detection task with and without transfer learning. Experimental results show that the Faster R-CNN model with transfer learning achieved the most successful result among the four models with an f1 score of 0.971.

Anahtar Kelimeler

• Object detection
• Adversarial attack
• Faster R-CNN
• YOLO v5
• CNN

Kaynakça

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