JOURNAL OF SUPERCOMPUTING, vol.1, pp.1-34, 2023 (SCI-Expanded)
In recent years, access to digital images has been made easy, and the
use and distribution of images have increased rapidly. Therefore,
undesirable adversarial attacks such as FGSM and PGD can occur during
the sharing or distribution of images. On the other hand, object
detection, one of the widely used applications of deep learning in real
life, is used to scan digital images to locate instances of every
object. However, the CNN and transformer-based deep learning models
trained using classical data augmentation may be unsatisfactory in
obtaining the required object detection accuracy on attacked images.
Therefore, to overcome the robustness problem of the deep learning-based
object detection methods against attacks, the deep learning approach
based on grayscale conversion and discrete wavelet transform is
proposed. The proposed approach uses well-known Faster R-CNN, YOLOv5,
and DETR as deep learning models. The performance of the proposed
approach using grayscale and DWT-based data augmentation has been
evaluated on the natural scene dataset containing street signs for
object detection on attacked images. Against FGSM attack with 0.10 𝜖 ,
f1 scores of Faster R-CNN, YOLOv5, and DETR models have increased by
0.18, 0.10, and 0.59, respectively. Also, against PGD attack with 0.10
𝜖 , f1 scores of models have increased, respectively, by 0.19, 0.23,
and 0.63 with the proposed technique. In addition, the performance of
the proposed approach has also been evaluated on the open dataset taken
from Kaggle. On the other hand, the memory size of the images processed
by the models and the run times of deep learning models has decreased.