Enhancing Adversarial Example Transferability with an Intermediate Level Attack

Understand

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.