Transparency Attacks: How Imperceptible Image Layers Can Fool AI
  Perception
Forrest McKee David Noever
This paper investigates a novel algorithmic vulnerability when imperceptible
image layers confound multiple vision models into arbitrary label assignments
and captions. We explore image preprocessing methods to introduce stealth
transparency, which triggers AI misinterpretation of what the human eye
perceives. The research compiles a broad attack surface to investigate the
consequences ranging from traditional watermarking, steganography, and
background-foreground miscues. We demonstrate dataset poisoning using the
attack to mislabel a collection of grayscale landscapes and logos using either
a single attack layer or randomly selected poisoning classes. For example, a
military tank to the human eye is a mislabeled bridge to object classifiers
based on convolutional networks (YOLO, etc.) and vision transformers (ViT,
GPT-Vision, etc.). A notable attack limitation stems from its dependency on the
background (hidden) layer in grayscale as a rough match to the transparent
foreground image that the human eye perceives. This dependency limits the
practical success rate without manual tuning and exposes the hidden layers when
placed on the opposite display theme (e.g., light background, light transparent
foreground visible, works best against a light theme image viewer or browser).
The stealth transparency confounds established vision systems, including
evading facial recognition and surveillance, digital watermarking, content
filtering, dataset curating, automotive and drone autonomy, forensic evidence
tampering, and retail product misclassifying. This method stands in contrast to
traditional adversarial attacks that typically focus on modifying pixel values
in ways that are either slightly perceptible or entirely imperceptible for both
humans and machines.