Enhancing Adversarial Example Transferability with an Intermediate Level Attack
This work addresses the challenge of enhancing adversarial example transferability for black-box attacks, which is incremental but important for security testing in machine learning.
The paper tackles the problem of adversarial examples overfitting to source models, reducing black-box transferability, and introduces the Intermediate Level Attack (ILA) to fine-tune adversarial examples by increasing perturbation on a pre-specified layer, improving upon state-of-the-art methods with high transferability.
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 are typically 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. We 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, improving upon state-of-the-art methods. We show that we can select a layer of the source model to perturb without any knowledge of the target models while achieving high transferability. Additionally, we provide some explanatory insights regarding our method and the effect of optimizing for adversarial examples using intermediate feature maps. Our code is available at https://github.com/CUVL/Intermediate-Level-Attack.