Two Heads Are Better Than One: Averaging along Fine-Tuning to Improve Targeted Transferability

• URL: http://arxiv.org/abs/2412.20807v1
• Date: Mon, 30 Dec 2024 09:01:27 GMT
• Title: Two Heads Are Better Than One: Averaging along Fine-Tuning to Improve Targeted Transferability
• Authors: Hui Zeng, Sanshuai Cui, Biwei Chen, Anjie Peng,
• Abstract summary: An adversarial example (AE) in feature space can efficiently boost its targeted transferability. Existing fine-tuning schemes only utilize the endpoint and ignore the valuable information in the fine-tuning trajectory. We propose averaging over the fine-tuning trajectory to pull the crafted AE towards a more centered region.
• Score: 20.46894437876869
• License:
• Abstract: With much longer optimization time than that of untargeted attacks notwithstanding, the transferability of targeted attacks is still far from satisfactory. Recent studies reveal that fine-tuning an existing adversarial example (AE) in feature space can efficiently boost its targeted transferability. However, existing fine-tuning schemes only utilize the endpoint and ignore the valuable information in the fine-tuning trajectory. Noting that the vanilla fine-tuning trajectory tends to oscillate around the periphery of a flat region of the loss surface, we propose averaging over the fine-tuning trajectory to pull the crafted AE towards a more centered region. We compare the proposed method with existing fine-tuning schemes by integrating them with state-of-the-art targeted attacks in various attacking scenarios. Experimental results uphold the superiority of the proposed method in boosting targeted transferability. The code is available at github.com/zengh5/Avg_FT.

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