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Research reveals input diversity can harm transfer attacks on robust models

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "The Scissors Effect" explores how input diversity techniques, specifically random resizing and padding, impact the effectiveness of transfer attacks in machine learning. The study, led by Yuhang Jiang, found that while these techniques generally improve attack success rates against standard models, they can actually degrade performance when used with adversarially trained models. This phenomenon, termed the "Scissors Effect," suggests that current evaluation methods may be underestimating the attack capabilities against robust models. AI

IMPACT This research highlights potential flaws in current adversarial robustness evaluation methods, suggesting that models may appear more robust than they are.

RANK_REASON Research paper published on arXiv detailing a new finding about adversarial attacks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research reveals input diversity can harm transfer attacks on robust models

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Research paper published on arXiv detailing a new finding about adversarial attacks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuhang Jiang, Xiaojing Chen ·

    The Scissors Effect: When Resize-Based Input Diversity Helps or Hurts Transfer Attacks

    arXiv:2606.22516v2 Announce Type: replace Abstract: Input Diversity (DI), a random resize and pad applied at each attack iteration, is a near-default ingredient of transfer-based attacks, widely assumed to improve transferability. We show this assumption is regime-dependent and, …

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