# Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior

arXiv: https://arxiv.org/abs/1907.01996 · HTML: https://mlsanity.com/paper/1907.01996/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://mlsanity.com/paper/2206.08304/) (2022)
- [The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches](https://mlsanity.com/paper/2604.26317/) (2026)

## Abstract (arXiv)

We present a novel method for generating robust adversarial image examples building upon the recent `deep image prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in image synthesis. Adversarial images are commonly generated by perturbing images to introduce high frequency noise that induces image misclassification, but that is fragile to subsequent digital manipulation of the image. We show that using DIP to reconstruct an image under adversarial constraint induces perturbations that are more robust to affine deformation, whilst remaining visually imperceptible. Furthermore we show that our DIP approach can also be adapted to produce local adversarial patches (`adversarial stickers'). We demonstrate robust adversarial examples over a broad gamut of images and object classes drawn from the ImageNet dataset.