Adversarial Attacks on Image Classification Models: Analysis and Defense
Jaydip Sen Abhiraj Sen Ananda Chatterjee
The notion of adversarial attacks on image classification models based on
convolutional neural networks (CNN) is introduced in this work. To classify
images, deep learning models called CNNs are frequently used. However, when the
networks are subject to adversarial attacks, extremely potent and previously
trained CNN models that perform quite effectively on image datasets for image
classification tasks may perform poorly. In this work, one well-known
adversarial attack known as the fast gradient sign method (FGSM) is explored
and its adverse effects on the performances of image classification models are
examined. The FGSM attack is simulated on three pre-trained image classifier
CNN architectures, ResNet-101, AlexNet, and RegNetY 400MF using randomly chosen
images from the ImageNet dataset. The classification accuracies of the models
are computed in the absence and presence of the attack to demonstrate the
detrimental effect of the attack on the performances of the classifiers.
Finally, a mechanism is proposed to defend against the FGSM attack based on a
modified defensive distillation-based approach. Extensive results are presented
for the validation of the proposed scheme.