  Deep neural networks (DNNs) have significantly advanced various domains, but
their vulnerability to adversarial attacks poses serious concerns.
Understanding these vulnerabilities and developing effective defense mechanisms
is crucial. DeepFool, an algorithm proposed by Moosavi-Dezfooli et al. (2016),
finds minimal perturbations to misclassify input images. However, DeepFool
lacks a targeted approach, making it less effective in specific attack
scenarios. Also, in previous related works, researchers primarily focus on
success, not considering how much an image is getting distorted; the integrity
of the image quality, and the confidence level to misclassifying. So, in this
paper, we propose Enhanced Targeted DeepFool, an augmented version of DeepFool
that allows targeting specific classes for misclassification and also introduce
a minimum confidence score requirement hyperparameter to enhance flexibility.
Our experiments demonstrate the effectiveness and efficiency of the proposed
method across different deep neural network architectures while preserving
image integrity as much and perturbation rate as less as possible. By using our
approach, the behavior of models can be manipulated arbitrarily using the
perturbed images, as we can specify both the target class and the associated
confidence score, unlike other DeepFool-derivative works, such as Targeted
DeepFool by Gajjar et al. (2022). Results show that one of the deep
convolutional neural network architectures, AlexNet, and one of the
state-of-the-art model Vision Transformer exhibit high robustness to getting
fooled. This approach can have larger implication, as our tuning of confidence
level can expose the robustness of image recognition models. Our code will be
made public upon acceptance of the paper.
