In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

Perturbation-based attacks, while not physically realizable, have been the main emphasis of
adversarial machine learning (ML) research. Patch-based attacks by contrast are physically …

This paper investigates a novel algorithmic vulnerability when imperceptible image layers
confound multiple vision models into arbitrary label assignments and captions. We explore …

The adversarial patch is a practical and effective method that modifies a small region on an
image, making DNNs fail to classify. Existing empirical defenses against adversarial patch …

Images posted online present a privacy concern in that they may be used as reference
examples for a facial recognition system. Such abuse of images is in violation of privacy …

In contrast to perturbation-based attacks, patch-based attacks are physically realizable, and
are therefore increasingly studied. However, prior work neglects the possibility of adaptive …

This investigation reveals a novel exploit derived from PNG image file formats, specifically
their alpha transparency layer, and its potential to fool multiple AI vision systems. Our …

Denial and deception (D&D) techniques that exploit misinformation and an adversary's
cognitive biases have long been a part of hybrid warfare. Such tactics cast uncertainty and …