Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Transferability of adversarial examples is of central importance for attacking an unknown
model, which facilitates adversarial attacks in more practical scenarios, eg, blackbox attacks …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Recent research finds CNN models for image classification demonstrate overlapped
adversarial vulnerabilities: adversarial attacks can mislead CNN models with small …

Achieving transferability of targeted attacks is reputed to be remarkably difficult. The current
state of the art has resorted to resource-intensive solutions that necessitate training model …

Vision transformers (ViTs) process input images as sequences of patches via self-attention;
a radically different architecture than convolutional neural networks (CNNs). This makes it …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

Transferability of adversarial examples is critical for black-box deep learning model attacks.
While most existing studies focus on enhancing the transferability of untargeted adversarial …

While effective, deep neural networks (DNNs) are vulnerable to adversarial attacks. In
particular, recent work has shown that such attacks could be generated by another deep …

In this paper, we use the interaction inside adversarial perturbations to explain and boost the
adversarial transferability. We discover and prove the negative correlation between the …