Comment on Transferability and Input Transformation with Additive Noise

• URL: http://arxiv.org/abs/2206.09075v1
• Date: Sat, 18 Jun 2022 00:52:27 GMT
• Title: Comment on Transferability and Input Transformation with Additive Noise
• Authors: Hoki Kim, Jinseong Park, Jaewook Lee
• Abstract summary: We analyze the relationship between transferability and input transformation with additive noise. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification of deep learning models.
• Score: 6.168976174718275
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification of deep learning models. More importantly, an adversarial example generated from a specific model can also deceive other models without modification. We call this phenomenon ``transferability". Here, we analyze the relationship between transferability and input transformation with additive noise by mathematically proving that the modified optimization can produce more transferable adversarial examples.

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