# Intermediate Level Adversarial Attack for Enhanced Transferability

arXiv: https://arxiv.org/abs/1811.08458 · HTML: https://mlsanity.com/paper/1811.08458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup](https://mlsanity.com/paper/2305.14846/) (2023)

## Abstract (arXiv)

Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversarial examples for one model can fool another model. However, adversarial examples may be overfit to exploit the particular architecture and feature representation of a source model, resulting in sub-optimal black-box transfer attacks to other target models. This leads us to introduce the Intermediate Level Attack (ILA), which attempts to fine-tune an existing adversarial example for greater black-box transferability by increasing its perturbation on a pre-specified layer of the source model. We show that our method can effectively achieve this goal and that we can decide a nearly-optimal layer of the source model to perturb without any knowledge of the target models.