Adversarial Attacks on Image Classification Models: FGSM and Patch
  Attacks and their Impact
Jaydip Sen Subhasis Dasgupta
This chapter introduces the concept of adversarial attacks on image
classification models built on convolutional neural networks (CNN). CNNs are
very popular deep-learning models which are used in image classification tasks.
However, very powerful and pre-trained CNN models working very accurately on
image datasets for image classification tasks may perform disastrously when the
networks are under adversarial attacks. In this work, two very well-known
adversarial attacks are discussed and their impact on the performance of image
classifiers is analyzed. These two adversarial attacks are the fast gradient
sign method (FGSM) and adversarial patch attack. These attacks are launched on
three powerful pre-trained image classifier architectures, ResNet-34,
GoogleNet, and DenseNet-161. The classification accuracy of the models in the
absence and presence of the two attacks are computed on images from the
publicly accessible ImageNet dataset. The results are analyzed to evaluate the
impact of the attacks on the image classification task.