Imperceptible Adversarial Attack on Deep Neural Networks from Image
  Boundary
Fahad Alrasheedi Xin Zhong
Although Deep Neural Networks (DNNs), such as the convolutional neural
networks (CNN) and Vision Transformers (ViTs), have been successfully applied
in the field of computer vision, they are demonstrated to be vulnerable to
well-sought Adversarial Examples (AEs) that can easily fool the DNNs. The
research in AEs has been active, and many adversarial attacks and explanations
have been proposed since they were discovered in 2014. The mystery of the AE's
existence is still an open question, and many studies suggest that DNN training
algorithms have blind spots. The salient objects usually do not overlap with
boundaries; hence, the boundaries are not the DNN model's attention.
Nevertheless, recent studies show that the boundaries can dominate the behavior
of the DNN models. Hence, this study aims to look at the AEs from a different
perspective and proposes an imperceptible adversarial attack that systemically
attacks the input image boundary for finding the AEs. The experimental results
have shown that the proposed boundary attacking method effectively attacks six
CNN models and the ViT using only 32% of the input image content (from the
boundaries) with an average success rate (SR) of 95.2% and an average peak
signal-to-noise ratio of 41.37 dB. Correlation analyses are conducted,
including the relation between the adversarial boundary's width and the SR and
how the adversarial boundary changes the DNN model's attention. This paper's
discoveries can potentially advance the understanding of AEs and provide a
different perspective on how AEs can be constructed.